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Funding: Bill & Melinda Gates Foundation., Peer Reviewed, Article signat per 1420 autors/es. Membres del GBD 2021 Forecasting Collaborators: Stein Emil Vollset*, Hazim S Ababneh, Yohannes Habtegiorgis Abate, Cristiana Abbafati, Rouzbeh Abbasgholizadeh, Mohammadreza Abbasian, Hedayat Abbastabar, Abdallah H A Abd Al Magied, Samar Abd ElHafeez, Atef Abdelkader, Michael Abdelmasseh, Sherief Abd-Elsalam, Parsa Abdi, Mohammad Abdollahi, Meriem Abdoun, Auwal Abdullahi, Mesfin Abebe, Olumide Abiodun, Richard Gyan Aboagye, Hassan Abolhassani, Mohamed Abouzid, Girma Beressa Aboye, Lucas Guimarães Abreu, Abdorrahim Absalan, Hasan Abualruz, Bilyaminu Abubakar, Hana Jihad Jihad Abukhadijah, Giovanni Addolorato, Victor Adekanmbi, Charles Oluwaseun Adetunji, Juliana Bunmi Adetunji, Temitayo Esther Adeyeoluwa, Rishan Adha, Ripon Kumar Adhikary, Qorinah Estiningtyas Sakilah Adnani, Leticia Akua Adzigbli, Fatemeh Afrashteh, Muhammad Sohail Afzal, Saira Afzal, Faith Agbozo, Antonella Agodi, Anurag Agrawal, Williams Agyemang-Duah, Bright Opoku Ahinkorah, Austin J Ahlstrom, Aqeel Ahmad, Firdos Ahmad, Muayyad M Ahmad, Sajjad Ahmad, Shahzaib Ahmad, Anisuddin Ahmed, Ayman Ahmed, Haroon Ahmed, Safoora Ahmed, Syed Anees Ahmed, Karolina Akinosoglou, Mohammed Ahmed Akkaif, Ashley E Akrami, Ema Akter, Salah Al Awaidy, Syed Mahfuz Al Hasan, Amjad S Al Mosa, Omar Al Ta’ani, Omar Ali Mohammed Al Zaabi, Fares Alahdab, Muaaz M Alajlani, Yazan Al-Ajlouni, Samer O Alalalmeh, Ziyad Al-Aly, Khurshid Alam, Noore Alam, Tahiya Alam, Zufishan Alam, Rasmieh Mustafa Al-amer, Fahad Mashhour Alanezi, Turki M Alanzi, Almaza Albakri, Wafa A Aldhaleei, Robert W Aldridge, Seyedeh Yasaman Alemohammad, Yihun Mulugeta Alemu, Adel Ali Saeed Al-Gheethi, Mohammed Khaled Al-Hanawi, Abid Ali, Amjad Ali, Iman Ali, Mohammed Usman Ali, Rafat Ali, Syed Shujait Shujait Ali, Victor Ekoche Ali, Waad Ali, Akram Al-Ibraheem, Gianfranco Alicandro, Sheikh Mohammad Alif, Syed Mohamed Aljunid, François Alla, Joseph Uy Almazan, Hesham M Al-Mekhlafi, Ahmed Yaseen Alqutaibi, Ahmad Alrawashdeh, S, Postprint (published version)

Transcranial magnetic stimulation and electroencephalography (TMS-EEG) recordings are crucial to directly assess cortical excitability and inhibition in a non-invasive and task-free manner. TMS-EEG signals are characterized by TMS-evoked potentials (TEPs), which are employed to evaluate cortical function. Nonetheless, different time windows (TW) have been used to compute them over the years. Moreover, these TWs tend to be the same for all participants omitting the intersubject variability. Therefore, the objective of this study is to assess the effect of using different TWs to compute the TEPs, moving from a common fixed TW to more adaptive individualized TWs. Twenty-nine healthy (HC) controls and twenty schizophrenia patients (SCZ) underwent single-pulse (SP) TMS-EEG protocol. Firstly, only the HC were considered to evaluate the TEPs for three different TWs in terms of amplitude and topographical distribution. Secondly, the SCZ patients were included to determine which TW is better to characterize the brain alterations of SCZ. The results indicate that a more individualized TW provides a better characterization of the SP TMS-EEG signals, although all of them show the same tendency. Regarding the comparison between groups, the individualized TW is the one that provides a better differentiation between populations. They also provide further support to the possible imbalance of cortical excitability/inhibition in the SCZ population due to its reduced activity in the N45 TEP and greater amplitude values in the N100. Results also suggest that the SCZ brain has a baseline hyperactive state since the TEPs of the SCZ appear earlier than those of the HC., This study is partially supported by the “Ministerio de Ciencia e Innovación (MICINN)” (grant ID PID2020-117751RB-I00), by the “Fundació La Marató de TV3” (grant ID 202219-30-31), by the “Gerencia Regional de Salud de Castilla y León” (grant ID GRS 2487/A/22), by the “Instituto de Salud Carlos III” (grant ID PI22/00465) and by the project HybridNeuro, a European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 101079392. GMM is in receipt of a FI-2022 grant from “Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR)”, AB is a Serra Húnter Fellow, and IFL is in receipt of predoctoral grants from the “Consejería de Educación, Junta de Castilla y León” (Spain) and the European Social Fund (grant IDs VA-183-18)., Peer Reviewed, Postprint (author's final draft)

Schizophrenia has been associated with a reduced task-related modulation of cortical activity assessed through electroencephalography (EEG). However, to the best of our knowledge, no study so far has assessed the underpinnings of this decreased EEG modulation in schizophrenia. A possible substrate of these findings could be a decreased inhibitory function, a replicated finding in the field. In this pilot study, our aim was to explore the association between EEG modulation during a cognitive task and the inhibitory system function in vivo in a sample including healthy controls and patients with schizophrenia. We hypothesized that the replicated decreased task-related activity modulation during a cognitive task in schizophrenia would be related to a hypofunction of the inhibitory system. For this purpose, 27 healthy controls and 22 patients with schizophrenia (including 13 first episodes) performed a 3-condition auditory oddball task from which the spectral entropy modulation was calculated. In addition, cortical reactivity—as an index of the inhibitory function—was assessed by the administration of 75 monophasic transcranial magnetic stimulation single pulses over the left dorsolateral prefrontal cortex. Our results replicated the task-related cortical activity modulation deficit in schizophrenia patients. Moreover, schizophrenia patients showed higher cortical reactivity following transcranial magnetic stimulation single pulses over the left dorsolateral prefrontal cortex compared to healthy controls. Cortical reactivity was inversely associated with EEG modulation, supporting the idea that a hypofunction of the inhibitory system could hamper the task-related modulation of EEG activity., Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This study has been supported by the “Gerencia Regional de Salud de Castilla y León” (grant IDs GRS 2029/A/19, GRS 2121/A/2020, and GRS 2487/A/22), “Instituto de Salud Carlos III” (grant ID PI22/00465), and partially supported by the “Ministerio de Ciencia e Innovación (MICINN)” (grant ID PID2020-117751RB-I00), and “Fundació La Marató de TV3” (grant ID 202219–30-31). IFL and RMBRS are in receipt of predoctoral grants from the “Consejería de Educación, Junta de Castilla y León” (Spain) and the European Social Fund (grant IDs VA-183–18 and VA-223–19, respectively). GMM is in receipt of a FI-2022 grant from “Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR)”, and AB is a Serra Húnter Fellow. Funding sources had no other role than financial support providers., Peer Reviewed, Postprint (published version)

Using mathematical models of physiological systems in medicine has allowed for the development of diagnostic, treatment, and medical educational tools. However, their complexity restricts, in most cases, their application for predictive, preventive, and personalized purposes. Although there are strategies that reduce the complexity of applying models based on fitting techniques, most of them are focused on a single instant of time, neglecting the effect of the system’s temporal evolution. The objective of this research was to introduce a dynamic fitting strategy for physiological models with an extensive array of parameters and a constrained amount of experimental data. The proposed strategy focused on obtaining better predictions based on the temporal trends in the system’s parameters and being capable of predicting future states. The study utilized a cardiorespiratory model as a case study. Experimental data from a longitudinal study of healthy adult subjects undergoing aerobic exercise were used for fitting and validation. The model predictions obtained in a steady state using the proposed strategy and the traditional single-fit approach were compared. The most successful outcomes were primarily linked to the proposed strategy, exhibiting better overall results regarding accuracy and behavior than the traditional population fitting approach at a single instant in time. The results evidenced the usefulness of the dynamic fitting strategy, highlighting its use for predictive, preventive, and personalized applications., Peer Reviewed, Postprint (published version)

Introduction: This study aims to discuss and assess the impact of three prevalent methodological biases: competing risks, immortal-time bias, and confounding bias in real-world observational studies evaluating treatment effectiveness. We use a demonstrative observational data example of COVID-19 patients to assess the impact of these biases and propose potential solutions. Methods: We describe competing risks, immortal-time bias, and time-fixed confounding bias by evaluating treatment effectiveness in hospitalized patients with COVID-19. For our demonstrative analysis, we use observational data from the registry of patients with COVID-19 who were admitted to the Bellvitge University Hospital in Spain from March 2020 to February 2021 and met our predefined inclusion criteria. We compare estimates of a single-dose, time-dependent treatment with the standard of care. We analyze the treatment effectiveness using common statistical approaches, either by ignoring or only partially accounting for the methodological biases. To address these challenges, we emulate a target trial through the clone-censor-weight approach. Results: Overlooking competing risk bias and employing the naïve Kaplan-Meier estimator led to increased in-hospital death probabilities in patients with COVID-19. Specifically, in the treatment effectiveness analysis, the Kaplan-Meier estimator resulted in an in-hospital mortality of 45.6% for treated patients and 59.0% for untreated patients. In contrast, employing an emulated trial framework with the weighted Aalen-Johansen estimator, we observed that in-hospital death probabilities were reduced to 27.9% in the “X”-treated arm and 40.1% in the non-“X”-treated arm. Immortal-time bias led to an underestimated hazard ratio of treatment. Conclusion: Overlooking competing risks, immortal-time bias, and confounding bias leads to shifted estimates of treatment effects. Applying the naïve Kaplan-Meier method resulted in the most biased results and overestimated pr, This study has been funded by the German Research Foundation (DFG, Deutsche Forschungsgemeinschaft) with project grant WO 1746/5-1 (MW), and Beatriu de Pinós post-doctoral programme from the Office of the Secretary of Universities and Research from the Ministry of Business and Knowledge of the Government of Catalonia programme: 2020 BP 00261 (HM); the Ministry of Science and Innovation [Ministerio de Ciencia e Innovación (MICINN)], Spain, under contract PID2020-117751RB-I00 (MAM, SR). CIBER-BBN is an initiative of the Instituto de Salud Carlos III, Spain., Peer Reviewed, Postprint (published version)

© BMJ Publishing Group Ltd, 2023. Reuse of this manuscript version (excluding any databases, tables, diagrams, photographs and other images or illustrative material included where a another copyright owner is identified) is permitted strictly pursuant to the terms of the Creative Commons Attribution-Non Commercial 4.0 International (CC-BY-NC 4.0) http://creativecommons.org, The manuscript by Martins Pinto-Filho et al., a multicentre study on the prognostic value of electrocardiographic findings, was evaluated. However, certain critical issues were highlighted. First, the authors didn't verify the randomness of missing data before implementing the Multiple Imputation by Chained Equations (MICE), which assumes randomness. Second, there was no use of external or internal validation despite the multicentre nature of the study. The study also lacks essential diagnostic measures, like pseudo-R2 statistics and the Hosmer-Lemeshow test. The time-dependent nature of ECG findings and the lack of consistent treatment strategy across different centers raises concerns. Furthermore, the study does not clarify how the imbalanced sample size influenced the conclusion. Lastly, the paper does not provide essential outputs and parameters like Receiver Operating Characteristic (ROC) and Harrell's C-index, which are important to avoid overfitting and ensure global applicability., This work was supported by the Beatriu de Pinós post- doctoral programme from the Office of the Secretary of Universities and Research from the Ministry of Business and Knowledge of the Government of Catalonia (Grant number: #2020 BP 00261)., Peer Reviewed, Objectius de Desenvolupament Sostenible::3 - Salut i Benestar, Objectius de Desenvolupament Sostenible::3 - Salut i Benestar::3.d - Reforçar la capacitat de tots els països, en particular els països en desenvolupament, en matèria d’alerta primerenca, reducció de riscos i gestió dels riscos per a la salut nacional i mundial, Postprint (author's final draft)

Background: Real-world observational data are an important source of evidence on the treatment effectiveness for patients hospitalized with coronavirus disease 2019 (COVID-19). However, observational studies evaluating treatment effectiveness based on longitudinal data are often prone to methodological biases such as immortal time bias, confounding bias, and competing risks. Methods: For exemplary target trial emulation, we used a cohort of patients hospitalized with COVID-19 (n¿=¿501) in a single centre. We described the methodology for evaluating the effectiveness of a single-dose treatment, emulated a trial using real-world data, and drafted a hypothetical study protocol describing the main components. To avoid immortal time and time-fixed confounding biases, we applied the clone-censor-weight technique. We set a 5-day grace period as a period of time when treatment could be initiated. We used the inverse probability of censoring weights to account for the selection bias introduced by artificial censoring. To estimate the treatment effects, we took the multi-state model approach. We considered a multi-state model with five states. The primary endpoint was defined as clinical severity status, assessed by a 5-point ordinal scale on day 30. Differences between the treatment group and standard of care treatment group were calculated using a proportional odds model and shown as odds ratios. Additionally, the weighted cause-specific hazards and transition probabilities for each treatment arm were presented. Results: Our study demonstrates that trial emulation with a multi-state model analysis is a suitable approach to address observational data limitations, evaluate treatment effects on clinically heterogeneous in-hospital death and discharge alive endpoints, and consider the intermediate state of admission to ICU. The multi-state model analysis allows us to summarize results using stacked probability plots that make it easier to interpret results. Conclusions: Extendi, Open Access funding enabled and organized by Projekt DEAL. This study has been funded by the German Research Foundation (original: Deutsche Forschungsgemeinschaft, DFG) with project grant WO 1746/5‑1; the Beatriu de Pinós post‑doctoral programme from the Office of the Secretary of Universities and Research from the Ministry of Business and Knowledge of the Government of Catalonia program (#2020 BP 00261) (HM)., Peer Reviewed, Postprint (published version)

Background Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Methods Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined; these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs; defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims; type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative, Peer Reviewed, Postprint (published version)

Methodological biases are common in observational studies evaluating treatment effectiveness. The objective of this study is to emulate a target trial in a competing risks setting using hospital-based observational data. We extend established methodology accounting for immortal time bias and time-fixed confounding biases to a setting where no survival information beyond hospital discharge is available: a condition common to coronavirus disease 2019 (COVID-19) research data. This exemplary study includes a cohort of 618 hospitalized patients with COVID-19. We describe methodological opportunities and challenges that cannot be overcome applying traditional statistical methods. We demonstrate the practical implementation of this trial emulation approach via clone–censor–weight techniques. We undertake a competing risk analysis, reporting the cause-specific cumulative hazards and cumulative incidence probabilities. Our analysis demonstrates that a target trial emulation framework can be extended to account for competing risks in COVID-19 hospital studies. In our analysis, we avoid immortal time bias, time-fixed confounding bias, and competing risks bias simultaneously. Choosing the length of the grace period is justified from a clinical perspective and has an important advantage in ensuring reliable results. This extended trial emulation with the competing risk analysis enables an unbiased estimation of treatment effects, along with the ability to interpret the effectiveness of treatment on all clinically important outcomes., This research was funded by the German Research Foundation (original: Deutsche Forschungsgemeinschaft), grant number WO 1746/5-1., Peer Reviewed, Postprint (published version)

Optimal allocation of ward beds is crucial given the respiratory nature of COVID-19, which necessitates urgent hospitalization for certain patients. Several governments have leveraged technology to mitigate the pandemic’s adverse impacts. Based on clinical and demographic variables assessed upon admission, this study predicts the length of stay (LOS) for COVID-19 patients in hospitals. The Kolmogorov–Gabor polynomial (a.k.a., Volterra functional series) was trained using regularized least squares and validated on a dataset of 1600 COVID-19 patients admitted to Khorshid Hospital in the central province of Iran, and the five-fold internal cross-validated results were presented. The Volterra method provides flexibility, interactions among variables, and robustness. The most important features of the LOS prediction system were inflammatory markers, bicarbonate (HCO3), and fever—the adj. R2 and Concordance Correlation Coefficients were 0.81 [95% CI: 0.79–0.84] and 0.94 [0.93–0.95], respectively. The estimation bias was not statistically significant (p-value = 0.777; paired-sample t-test). The system was further analyzed to predict “normal” LOS = 7 days versus “prolonged” LOS > 7 days groups. It showed excellent balanced diagnostic accuracy and agreement rate. However, temporal and spatial validation must be considered to generalize the model. This contribution is hoped to pave the way for hospitals and healthcare providers to manage their resources better., This research was funded by the Beatriu de Pinós post-doctoral programme from the Office of the Secretary of Universities and Research from the Ministry of Business and Knowledge of the Government of Catalonia programme: 2020 BP 00261 (H.M.); National Funds through the FCT—Fundação para a Ciência e a Tecnologia, I.P., under the scope of the projects UIDB/00297/2020 and UIDP/00297/2020 (Center for Mathematics and Applications) (M.N.); the Ministry of Science and Innovation [Ministerio de Ciencia e Innovación (MICINN)], Spain, under contract PID2020-117751RBI00 (M.A.M., S.R.L.). CIBER-BBN is an initiative of the Instituto de Salud Carlos III, Spain. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript., Peer Reviewed, Postprint (published version)

© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic., PURPOSE: The objective of this study was to quantitatively evaluate psychological and quality of life-related complications at three months following discharge in hospitalized coronavirus disease 2019 (COVID-19) patients during the pandemic in Iran. METHODS: In this time-point analysis of prospective cohort study data, adult patients hospitalized with symptoms suggestive of COVID-19 were enrolled. Patients were stratifed in analyses based on severity. The primary outcomes consisted of psychological problems and pulmonary function tests (PFTs) in the three months following discharge, with Health-related quality of life (HRQoL) as the secondary outcome. Exploratory predictors were determined for both primary and secondary outcomes. Results 283 out of 900 (30%) eligible patients were accessible for the follow-up assessment and included in the study. The mean age was 53.65±13.43 years, with 68% experiencing a severe disease course. At the time of the fnal follow-up, participants still reported persistent symptoms, among which fatigue, shortness of breath, and cough were the most common. Based on the regression-adjusted analysis, lower levels of forced expiratory volume in one second (FEV1) to forced vital capacity (FVC) ratio was associated with higher levels of depression (standardized ß=- 0.161 (SE=0.042), P=0.017) and stress levels (standardized ß=- 0.110 (SE=0.047), P=0.015). Furthermore, higher levels of anti-SARS-CoV-2 immunoglobulinM (IgM) were associated with signifcantly lower levels of depression (standardized ß=- 0.139 (SE=0.135), P=0.031). CONCLUSIONS: There is an association between lung damage during COVID-19 and the reduction of pulmonary function for up to three months from acute infection in hospitalized patients. Varying degrees of anxiety, depression, stress, and low HRQoL frequently occur in patients with COVID-19. More severe lung damage and lower COVID-19 antibodies were associated with lower levels of psychological health., Isfahan University of Medical Sciences, IR.MUI.MED.REC.1399.517, Ramin Sami, Peer Reviewed, Postprint (published version)

Industry 4.0 signifies a new step in the control and organization of the manufacturing value chain. It can provide better digital solutions for our day-to-day lives during pandemics and post-pandemic life, including better activity planning, global public health emergency, and risk assessment. Internet of Things (IoT), as a part of Industry 4.0, brings new opportunities in various applications, including smart healthcare and cities. The combination of an IoT system and Artificial Intelligence (AI) has the following capabilities in pandemic management: public security improvement, contact tracing to access public places, and automated diagnosis and post-pandemic prognosis. In this chapter, we discuss the contribution of Industry 4.0 (IoT, digitalization, Big Data, AI, and cloud-based computing) for automatic surveillance and health monitoring during pandemics and post-pandemic life. More than fifty papers published in 2020-2022 were analyzed in this chapter., This work was supported by the Beatriu de Pinós post-doctoral programme from the Office of the Secretary of Universities and Research from the Ministry of Business and Knowledge of the Government of Catalonia (Grant number: #2020 BP 00261) and the TecnioSpring Industry fellowship (ACCIO, H2020-EU- EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions, #ACE026/21/000035)., Peer Reviewed, Objectius de Desenvolupament Sostenible::3 - Salut i Benestar, Objectius de Desenvolupament Sostenible::3 - Salut i Benestar::3.d - Reforçar la capacitat de tots els països, en particular els països en desenvolupament, en matèria d’alerta primerenca, reducció de riscos i gestió dels riscos per a la salut nacional i mundial, Postprint (author's final draft)

Low-volume lung injury encompasses local concentration of stresses in the vicinity of collapsed regions in heterogeneously ventilated lungs. We aimed to study the effects on ventilation and perfusion distributions of a sequential lateral positioning (30°) strategy using electrical impedance tomography imaging in a porcine experimental model of early acute respiratory distress syndrome (ARDS). We hypothesized that such strategy, including a real-time individualization of positive end-expiratory pressure (PEEP) whenever in lateral positioning, would provide attenuation of collapse in the dependent lung regions. A two-hit injury acute respiratory distress syndrome experimental model was established by lung lavages followed by injurious mechanical ventilation. Then, all animals were studied in five body positions in a sequential order, 15 min each: Supine 1; Lateral Left; Supine 2; Lateral Right; Supine 3. The following functional images were analyzed by electrical impedance tomography: ventilation distributions and regional lung volumes, and perfusion distributions. The induction of the acute respiratory distress syndrome model resulted in a marked fall in oxygenation along with low regional ventilation and compliance of the dorsal half of the lung (gravitational-dependent in supine position). Both the regional ventilation and compliance of the dorsal half of the lung greatly increased along of the sequential lateral positioning strategy, and maximally at its end. In addition, a corresponding improvement of oxygenation occurred. In conclusion, our sequential lateral positioning strategy, with sufficient positive end-expiratory pressure to prevent collapse of the dependent lung units during lateral positioning, provided a relevant diminution of collapse in the dorsal lung in a porcine experimental model of early acute respiratory distress syndrome., Postprint (published version)

Applying complex mathematical models of physiological systems is challenging due to the large number of parameters. Identifying these parameters through experimentation is difficult, and although procedures for fitting and validating models are reported, no integrated strategy exists. Additionally, the complexity of optimization is generally neglected when the number of experimental observations is restricted, obtaining multiple solutions or results without physiological justification. This work proposes a fitting and validation strategy for physiological models with many parameters under various populations, stimuli, and experimental conditions. A cardiorespiratory system model is used as a case study, and the strategy, model, computational implementation, and data analysis are described. Using optimized parameter values, model simulations are compared to those obtained using nominal values, with experimental data as a reference. Overall, a reduction in prediction error is achieved compared to that reported for model building. Furthermore, the behavior and accuracy of all the predictions in the steady state were improved. The results validate the fitted model and provide evidence of the proposed strategy’s usefulness., Peer Reviewed, Postprint (published version)

© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., Objective : Spike sorting of muscular and neural recordings requires separating action potentials that overlap in time (superimposed action potentials (APs)). We propose a new algorithm for resolving superimposed action potentials, and we test it on intramuscular EMG (iEMG) and intracortical recordings. Methods : Discrete-time shifts of the involved APs are first selected based on a heuristic extension of the peel-off algorithm. Then, the time shifts that provide the minimal residual Euclidean norm are identified (Discrete Brute force Correlation (DBC)). The optimal continuous-time shifts are then estimated (High-Resolution BC (HRBC)). In Fusion HRBC (FHRBC), two other cost functions are used. A parallel implementation of the DBC and HRBC algorithms was developed. The performance of the algorithms was assessed on 11,000 simulated iEMG and 14,000 neural recording superpositions, including two to eight APs, and eight experimental iEMG signals containing four to eleven active motor units. The performance of the proposed algorithms was compared with that of the Branch-and-Bound (BB) algorithm using the Rank-Product (RP) method in terms of accuracy and efficiency. Results : The average accuracy of the DBC, HRBC and FHRBC methods on the entire simulated datasets was 92.16±17.70, 93.65±16.89, and 94.90±15.15 (%). The DBC algorithm outperformed the other algorithms based on the RP method. The average accuracy and running time of the DBC algorithm on 10.5 ms superimposed spikes of the experimental signals were 92.1±21.7 (%) and 2.3±15.3 (ms). Conclusion and Significance : The proposed algorithm is promising for real-time neural decoding, a central problem in neural and muscular decoding and interfacing., The work of Hamid R. Marateb was supported by the Office of the Secretary of Universities and Research from the Ministry of Business and Knowledge of the Government of Catalonia Programme through Beatriu de Pinós PostDoctoral Programme under Grant 2020 BP 00261. The work of Mehdi Shirzad and Miguel A. Mañanas was supported by the Spanish Ministry of Science and Innovation under Project PID2020-117751RB-I00. The work of Silvia Muceli was supported by the Chalmers Life Science Engineering Area of Advance and the European Union’s Horizon 2020 Research and Innovation Program under the Marie Sklodowska-Curie Grant Agreement 846679 (INFANTPATTERNS). The work of Dario Farina was supported by European Research Council Synergy Project Natural BionicS under Grant 810346., Peer Reviewed, Postprint (published version)

Deep learning has demonstrated excellent results for ECG anomaly detection, wherein most approaches used supervised learning. The requirement of thousands of manually annotated samples is a concern for state-of-the-art anomaly detection systems, especially for fetal ECG (FECG), and currently, there is not a publicly available FECG dataset annotated for each FECG beat. In this paper, we offer a modified active learning technique based on transfer learning, calibration probability, and autoencoder-based sampling to reduce number of samples requires to annotate. In this regard, we used 25,000 s of recording from 47 patients from the MIT-BIH Arrhythmia Database to train a deep learning model to detect anomalies in non-fetus subjects. Then we used the unlabeled Non-Invasive Fetal ECG Arrhythmia Database (NIFEA DB) of 26 subjects to fine-tune the trained model to fine-tune the trained model based on active learning to detect anomalies in binary form for fetal. A variational autoencoder is trained on all data (adult and fetal ECG), and clustering is applied to latent features extracted from data after dimension reduction. Then, the sampling process of active learning selected samples from different clusters with low confidence to cover all data distribution. Moreover, a probability calibration based on mc-dropout and isotonic regression is used to calibrate confidences, helping to select reliable low-confidence samples. Various ablation studies were performed to show the influence of autoencoder-based sampling, calibration, and transfer learning, which showed that the proposed method could achieve 92% accuracy using 399 training samples. In contrast, other methods required more training samples to reach the same level of accuracy without calibration or an autoencoder and clustering approach or training without active learning. The study also found that transfer learning significantly impacted faster convergence and that the proposed active learning approach was more effect, The authors thank Dr Samani for annotating data and acknowledge funding from the Natural Sciences and Engineering Research Council of Canada (NSERC) to support the work., Peer Reviewed, Postprint (published version)

High-Density Surface Electromyography (HD-sEMG) is a non-invasive technique for measuring the electrical activity of a muscle with multiple, closely spaced electrodes. Estimation of muscle force is one of the applications of HD-sEMG. Usually, validating different EMG-Force models entails simple movements limited to laboratory settings. The validity of these models in more ecological conditions, requesting force production over a wide frequency band, remains unknown. In this study, we, therefore, compare the results of force prediction using four different types of input force profiles that can be representative of daily life activities, and we investigate whether the crest factor of these different input signals affects force prediction. For predicting the force from sEMG signals, we used our real-time and convex methods. HD-sEMG signals were recorded with 144 channels from the biceps brachii, brachioradialis, and triceps (long, lateral, and medial head) muscles of 24 healthy subjects during random signal, random phase, Schroeder phase, and minimum crest factor (crestmin) signal. The correlation and coefficient of determination (R 2 ) between measured and predicted forces were calculated for the different force feedback profiles. The crestmin signal showed significantly better results based on statistical tests (P-value < 0.05), with correlation and R 2 equal to 0.92±0.03 and 0.86±0.05, respectively. The results demonstrate that the crest factor of input signals is a crucial parameter that can impact the performance of EMG-Force models and must be considered during training.Clinical Relevance— This study demonstrates that lower crest factor multisine force profiles result in improved fitness for force prediction and can be used as an alternative to random signals., Research supported by Spanish Innovation and Science Ministry from the project with ref PDC2021-120818-I00., Peer Reviewed, Postprint (published version)

Rett syndrome (RTT) is the second leading cause of mental disability in women and causes a serious neurodevelopmental disorder that leads to a global disability of the individual. Cognitive training has been shown to improve neuropsychological and neurophysiological parameters in similar pathologies. However, knowledge about neurophysiological training in RTT is scarce. The objective of the study was to evaluate the result and duration of the effect of cognitive stimulation over four consecutive days of training, in order to determine the viability of a long-term effect. Nineteen patients performed a cognitive task using an eye tracking device during the first three days of training. Prior to each training, a resting state EEG with eyes open was recorded to assess changes in spectral variables in the brain. An EEG recording at rest with eyes open was also acquired on the fourth day, in order to evaluate the differences with respect to the first day. Results showed a spectral shift to higher frequencies in a long-term period of four days. Significant positive alterations of brain rhythms were observed: decrease in low-frequency (delta) oscillations and an increase in high-frequency (beta) rhythms, resulting in more complex signals closer to non-pathological brain behavior. In conclusion, this study showed the potential long-term benefits of cognitive stimulation in RTT patients., Postprint (published version)

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., A non-invasive fetal electrocardiogram (FECG) is used to monitor the electrical pulse of the fetal heart. Decomposing the FECG signal from the maternal ECG (MECG) is a blind source separation problem, which is hard due to the low amplitude of the FECG, the overlap of R waves, and the potential exposure to noise from different sources. Traditional decomposition techniques, such as adaptive filters, require tuning, alignment, or pre-configuration, such as modeling the noise or desired signal to map the MECG to the FECG. The high correlation between maternal and fetal ECG fragments decreases the performance of convolution layers. Therefore, the masking region of interest based on the attention mechanism was performed to improve the signal generators' precision. The sine activation function was also used to retain more details when converting two signal domains. Three available datasets from the Physionet, including the A&D FECG, NI-FECG, and NI-FECG challenge, and one synthetic dataset using FECGSYN toolbox, were used to evaluate the performance. The proposed method could map an abdominal MECG to a scalp FECG with an average of 98% R-Square [CI 95%: 97%, 99%] as the goodness of fit on the A&D FECG dataset. Moreover, it achieved 99.7% F1-score [CI 95%: 97.8-99.9], 99.6% F1-score [CI 95%: 98.2%, 99.9%] and 99.3% F1-score [CI 95%: 95.3%, 99.9%] for fetal QRS detection on the A&D FECG, NI-FECG and NI-FECG challenge datasets, respectively. Also, the distortion was in the “very good” and “good” ranges. These results are comparable to the state-of-the-art results; thus, the proposed algorithm has the potential to be used for high-performance signal-to-signal conversion., Peer Reviewed, Postprint (author's final draft)

Background: The mechanisms through which kappa opioid receptor (KOR) agonists induce psychotomimetic effects are largely unknown, although the modulation of this receptor has attracted attention for its clinical use. In this work, we characterize the neuropharmacological effects of salvinorin-A, a highly selective KOR agonist. Methods: Changes in multimodal electroencephalography, single-photon emission computed tomography, and subjective effects following the acute administration of salvinorin-A are reported. The study included 2 sub-studies that employed a double-blind, crossover, randomized, placebo-controlled design. Results The electroencephalography measures showed a marked increase in delta and gamma waves and a decrease in alpha waves while subjects were under the effect of salvinorin-A. Regarding single-photon emission computed tomography measures, significant decreases in regional cerebral blood flow were detected in multiple regions of the frontal, temporal, parietal, and occipital cortices. Significant regional cerebral blood flow increases were observed in some regions of the medial temporal lobe, including the amygdala, the hippocampal gyrus, and the cerebellum. The pattern of subjective effects induced by salvinorin-A was similar to those observed in relation to other psychotomimetic drugs but with an evidently dissociative nature. No dysphoric effects were reported. Conclusion: The salvinorin-A–mediated KOR agonism induced dramatic psychotomimetic effects along with a generalized decrease in cerebral blood flow and electric activity within the cerebral cortex., Postprint (author's final draft)

The data that support the findings of this study are openly available at the following URL/DOI: https://doi.org/10.1371/journal.pone.0182578., Objective. Improvements in electroencephalography enable the study of the localization of active brain regions during motor tasks. Movement-related cortical potentials (MRCPs), and event-related desynchronization (ERD) and synchronization are the main motor-related cortical phenomena/neural correlates observed when a movement is elicited. When assessing neurological diseases, averaging techniques are commonly applied to characterize motor related processes better. In this case, a large number of trials is required to obtain a motor potential that is representative enough of the subject’s condition. This study aimed to assess the effect of a limited number of trials on motor-related activity corresponding to different upper limb movements (elbow flexion/extension, pronation/supination and hand open/close). Approach. An open dataset consisting on 15 healthy subjects was used for the analysis. A Monte Carlo simulation approach was applied to analyse, in a robust way, different typical time- and frequency-domain features, topography, and low-resolution electromagnetic tomography. Main results. Grand average potentials, and topographic and tomographic maps showed few differences when using fewer trials, but shifts in the localization of motor-related activity were found for several individuals. MRCP and beta ERD features were more robust to a limited number of trials, yielding differences lower than 20% for cases with 50 trials or more. Strong correlations between features were obtained for subsets above 50 trials. However, the inter-subject variability increased as the number of trials decreased. The elbow flexion/extension movement showed a more robust performance for a limited number of trials, both in population and in individual-based analysis. Significance. Our findings suggested that 50 trials can be an appropriate number to obtain stable motor-related features in terms of differences in the averaged motor features, correlation, and changes in topography and tomog, This study has been funded by the Ministry of Science and Innovation (MICINN), Spain, under contract PID2020-117751RB-I00. CIBER-BBN is an initiative of the Instituto de Salud Carlos III, Spain. Marta Borràs gratefully acknowledges the Universitat Politècnica de Catalunya and Banco Santander for the financial support of her predoctoral Grant FPI-UPC. A B is a Serra Hunter Fellow., Peer Reviewed, Postprint (published version)

The subject of respiratory mechanics has complex characteristics, functions, and interactions that can be difficult to understand in training and medical education contexts. As such, education strategies based on computational simulations comprise useful tools, but their application in the medical area requires stricter validation processes. This paper shows a statistical and a Delphi validation for two modules of a web application used for respiratory system learning: (I) “Anatomy and Physiology” and (II) “Work of Breathing Indexes”. For statistical validation, population and individual analyses were made using a database of healthy men to compare experimental and model-predicted data. For both modules, the predicted values followed the trend marked by the experimental data in the population analysis, while in the individual analysis, the predicted errors were 9.54% and 25.38% for maximal tidal volume and airflow, respectively, and 6.55%, 9.33%, and 11.77% for rapid shallow breathing index, work of breathing, and maximal inspiratory pressure, respectively. For the Delphi validation, an average higher than 4 was obtained after health professionals evaluated both modules from 1 to 5. In conclusion, both modules are good tools for respiratory system learning processes. The studied parameters behaved consistently with the expressions that describe ventilatory dynamics and were correlated with experimental data; furthermore, they had great acceptance by specialists., Peer Reviewed, Postprint (published version)

© Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ., Objective : There is a lack of sensitive and specific biomarkers for use in progressive multiple sclerosis (MS). The study aimed to assess the potential of serum neurofilament light chain (sNfL) levels as biomarker of disability progression in patients with progressive MS. Methods: We performed a prospective observational cohort study in 51 patients with progressive MS who participated in a 2-year phase II single-centre, randomised, double-blind, placebo-controlled trial of interferon-beta. Mean (SD) follow-up duration was 13.9 (6.2) years. Levels of sNfL were measured using a single molecule array immunoassay at baseline, 1, 2 and 6 years. Univariable and multivariable analyses were carried out to evaluate associations between sNfL levels and disability progression at short term (2 years), medium term (6 years) and long term (at the time of the last follow-up). Results: A sNfL cut-off value of 10.2¿pg/mL at baseline discriminated between long-term progressors and non-progressors with a 75% sensitivity and 67% specificity (adjusted OR 7.8; 95%¿CI 1.8 to 46.4; p=0.01). Similar performance to discriminate between long-term progressors and non-progressors was observed using age/body mass index-adjusted sNfL Z-scores derived from a normative database of healthy controls. A cut-off increase of 5.1¿pg/mL in sNfL levels between baseline and 6 years also discriminated between long-term progressors and non-progressors with a 71% sensitivity and 86% specificity (adjusted OR 49.4; 95%¿CI 4.4 to 2×103; p=0.008). Conclusions: sNfL can be considered a prognostic biomarker of future long-term disability progression in patients with progressive MS. These data expand the little knowledge existing on the role of sNfL as long-term prognostic biomarker in patients with progressive MS., Postprint (author's final draft)

Transcranial magnetic stimulation and electroencephalography (TMS-EEG) recordings are key to perform a direct evaluation of cortical function in a non-invasive and task-free manner. For the past few years, it has been used to study different brain disorders, such as schizophrenia (SCZ). Nonetheless, TMS-EEG signals present a huge artefact due to the magnetic pulse making its cleaning a challenge. In this study, TMS-EEG is used to assess SCZ patients following different types of pulses. The objective of the study is twofold: to propose a different approach to obtain a clean signal; and to characterise it to compare the inhibitory activity of different groups of subjects regarding the TMS-evoked potentials (TEPs). The novelty independent component representation of TMS-EEG has led us to obtain a clean signal whose characterisation has allowed us to study the inhibitory activity. TEPs obtained match those from the literature: healthy control group shows more inhibition than the schizophrenic patients’ group. Allowing the validation of the proposed TMS-EEG cleaning methodology., This research was partially supported by: the ‘Ministerio de Ciencia e Innovación’ under project PID2020- 117751RB; the ‘Junta de Castilla y León’ under project GRS 2121/AA/2020; and the ‘Instituto de salúd Carlos III’ under project PI 18/00178. GMM is in receipt of a FI-2022 grant from ‘Agència de Gestió d'Ajuts Universitaris i de Recerca (AGAUR)’; IFL is in receipt of a predoctoral grant from the "Consejería de Educación, Junta de Castilla y León" (Spain), the European Social Fund (grant ID VA183-18); finally, AB and JFA are Serra Húnter Fellows., Peer Reviewed, Postprint (published version)

Chapter 3 discusses the role of sensors in cases of moderate COVID-19 infection that may be linked to cognitive impairments; such prediction systems are promising for smart healthcare design., This work was supported by the Beatriu de Pinós post-doctoral programme from the Office of the Secretary of Universities and Research from the Ministry of Business and Knowledge of the Government of Catalonia (Grant number: #2020 BP 00261), and the TecnioSpring Industry fellowship (ACCIO, H2020-EU- EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions, #ACE026/21/000035)., Peer Reviewed, Postprint (author's final draft)

The performance of myoelectric control highly depends on the features extracted from surface electromyographic (sEMG) signals. We propose three new sEMG features based on the kernel density estimation. The trimmed mean of density (TMD), the entropy of density, and the trimmed mean absolute value of derivative density were computed for each sEMG channel. These features were tested for the classification of single tasks as well as of two tasks concurrently performed. For single tasks, correlation-based feature selection was used, and the features were then classified using linear discriminant analysis (LDA), non-linear support vector machines, and multi-layer perceptron. The eXtreme gradient boosting (XGBoost) classifier was used for the classification of two movements simultaneously performed. The second and third versions of the Ninapro dataset (conventional control) and Ameri’s movement dataset (simultaneous control) were used to test the proposed features. For the Ninapro dataset, the overall accuracy of LDA using the TMD feature was 98.99 ± 1.36% and 92.25 ± 9.48% for able-bodied and amputee subjects, respectively. Using ensemble learning of the three classifiers, the average macro and micro-F-score, macro recall, and precision on the validation sets were 98.23 ± 2.02, 98.32 ± 1.93, 98.32 ± 1.93, and 98.88 ± 1.31%, respectively, for the intact subjects. The movement misclassification percentage was 1.75 ± 1.73 and 3.44 ± 2.23 for the intact subjects and amputees. The proposed features were significantly correlated with the movement classes [Generalized Linear Model (GLM); P-value < 0.05]. An accurate online implementation of the proposed algorithm was also presented. For the simultaneous control, the overall accuracy was 99.71 ± 0.08 and 97.85 ± 0.10 for the XGBoost and LDA classifiers, respectively. The proposed features are thus promising for conventional and simultaneous myoelectric control., Peer Reviewed, Postprint (published version)

Purpose: To qualitatively and quantitatively compare synthetic and conventional MRI sequences acquired on a 1.5-T system for patients with multiple sclerosis (MS). Methods: Prospective study that involved twenty-seven consecutive relapsing–remitting MS patients scanned on a 1.5-T MRI scanner. The MRI protocol included 2D transverse conventional spin-echo sequences: proton density-weighted (PD), T2-weighted, T2-FLAIR, and T1-weighted. Synthetic images were generated using 2D transverse QRAPMASTER and SyMRI software with the same voxel size, repetition, echo, and inversion times as the conventional sequences. Four raters performed a crosstab qualitative analysis that involved evaluating global image quality, contrast, flow artefacts, and confidence in lesion assessment introducing the concepts of predominance, agreement, and disagreement. A quantitative analysis was also performed and included evaluating the number of lesions (periventricular, juxtacortical, brainstem, and cerebellum) and the contrast-to-noise ratio between regions (CSF, white matter, grey matter, lesions). Results: The global image quality assessment showed predominance for better scores for conventional sequences over syn- thetic sequences, whereas contrast, confidence in lesion assessment, and flow artefacts showed predominance for agreement between sequences. There was predominance for disagreement between all pairs of raters in most of the evaluated qualitative parameters. Synthetic PD and T2-FLAIR images showed higher contrast-to-noise ratios than the corresponding conventional images for most comparison between regions. There were no significant differences in the number of lesions detected for most of the study regions between conventional and synthetic images. Conclusion: Synthetic MRI can be potentially used as an alternative to conventional brain MRI sequences in the assessment of MS., Postprint (author's final draft)

Es habitual que planes de estudio para titulaciones de grado incluyan una asignatura transversal de informá- tica en el primer año. En los estudios de Náutica encontramos un perfil de alumnado que percibe la asignatura ajena a la profesión escogida, lo que se refleja en una baja motivación. Para dar respuesta a esta realidad, con el objetivo de aumentar la motiva- ción del alumnado y contextualizar la materia en el centro de interés, se ha llevado a cabo una experien- cia de aprendizaje basado en proyectos (ABP). El reto planteado consiste en elaborar, en grupos reducidos, un programa funcional que dé respuesta a alguna de las necesidades propias del ámbito de la náutica. Durante el proceso, se elabora un diario para tomar consciencia del proceso de trabajo y de los aprendiza- jes. Al final, cada grupo presenta el proyecto a sus compañeros. Se ha valorado la satisfacción del alum- nado en relación a la metodología docente y su per- cepción de aprendizaje mediante cuestionarios, com- parándolos con un grupo control, que ha seguido una metodología expositiva participativa. Los resultados evidencian que la metodología ABP da lugar a una mayor satisfacción y a una percepción de un aprendi- zaje más profundo. La experiencia es generalizable a otras titulaciones con poco carácter tecnológico, siendo necesario adaptar el centro de interés. Para facilitar dicha tarea, se hace público el material gene- rado en la presente experiencia docente., Postprint (published version)

The use of physiological models in medicine allows the evaluation of new hypotheses, development of diagnosis and clinical treatment applications, and development of training and medical education tools, as well as medical device design. Although several mathematical models of physiological systems have been presented in the literature, few of them are able to predict the human cardiorespiratory response under physical exercise stimulus adequately. This paper aims to present the building and comparison of an integrated cardiorespiratory model focused on the prediction of the healthy human response under rest and aerobic exercise. The model comprises cardiovascular circulation, respiratory mechanics, and gas exchange system, as well as cardiovascular and respiratory controllers. Every system is based on previously reported physiological models and incorporates reported mechanisms related to the aerobic exercise dynamics. Experimental data of 30 healthy male volunteers undergoing a cardiopulmonary exercise test and simulated data from two of the most current and complete cardiorespiratory models were used to evaluate the performance of the presented model. Experimental design, processing, and exploratory analysis are described in detail. The simulation results were compared against the experimental data in steady state and in transient regime. The predictions of the proposed model closely mimic the experimental data, showing in overall the lowest prediction error (10.35%), the lowest settling times for cardiovascular and respiratory variables, and in general the fastest and similar responses in transient regime. These results suggest that the proposed model is suitable to predict the cardiorespiratory response of healthy adult humans under rest and aerobic exercise conditions., Peer Reviewed, Postprint (published version)

Objective: High-frequency oscillations (HFOs) have emerged as a promising clinical biomarker for presurgical evaluation in childhood epilepsy. HFOs are commonly classified in stereo-encephalography as ripples (80–200 Hz) and fast ripples (200–500 Hz). Ripples are less specific and not so directly associated with epileptogenic activity because of their physiological and pathological origin. The aim of this paper is to distinguish HFOs in the ripple band and to improve the evaluation of the epileptogenic zone (EZ). Approach: This study constitutes a novel modeling approach evaluated in ten patients from Sant Joan de Deu Pediatric Hospital (Barcelona, Spain), with clearly-defined seizure onset zones (SOZ) during presurgical evaluation. A subject-by-subject basis analysis is proposed: a probabilistic Gaussian mixture model (GMM) based on the combination of specific ripple features is applied for estimating physiological and pathological ripple subpopulations. Main Results: Clear pathological and physiological ripples are identified. Features differ considerably among patients showing within-subject variability, suggesting that individual models are more appropriate than a traditional whole-population approach. The difference in rates inside and outside the SOZ for pathological ripples is significantly higher than when considering all the ripples. These significant differences also appear in signal segments without epileptiform activity. Pathological ripple rates show a sharp decline from SOZ to non-SOZ contacts and a gradual decrease with distance. Significance: This novel individual GMM approach improves ripple classification and helps to refine the delineation of the EZ, as well as being appropriate to investigate the interaction of epileptogenic and propagation networks., This work was supported by Ministry of Science and Innovation (MICIIN), Spain, under Contract PID2020-117751RB-I00., Peer Reviewed, Postprint (published version)

e-Health may enhance self-management of pelvic floor muscle training (PFMT) to treat stress urinary incontinence (SUI). It is crucial to involve patients in planning, developing and monitoring the optimal e-Health solution. This research aims to describe patient-centered innovation in an early developmental stage of the WOMEN-UP solution. We conducted a qualitative study through a self-developed questionnaire in 22 women with SUI, to define system requirements from a patient’s perspective. The first prototype of the WOMEN-UP solution was developed. It was tested by 9 patients in a usability study (think-aloud protocol and retrospective interviews). Patient preferences regarding the possible use of an e-Health solution with serious games for PFMT were: (1) to receive feedback about PFMT; (2) convenient home-use; (3) increasing motivation; (4) available in medical centers. Identified usability aids (31) reassured our design-development plan, which considered the biofeedback and serious games as key factors. Patient’s perspective detected some unexpected issues related to the calibration and serious games, involving a change in the ongoing development to get an improved WOMEN-UP solution; the value of patient-centered innovation during the development of an e-Health solution for PFMT (WOMEN-UP solution). To identify patients’ unmet needs, we proposed a longitudinal approach for the future eHealth-related patient-centered innovations., Peer Reviewed, Postprint (published version)

Background: Already at hospital admission, clinicians require simple tools to identify hospitalized COVID-19 patients at high risk of mortality. Such tools can significantly improve resource allocation and patient management within hospitals. From the statistical point of view, extended time-to-event models are required to account for competing risks (discharge from hospital) and censoring so that active cases can also contribute to the analysis. // Methods: We used the hospital-based open Khorshid COVID Cohort (KCC) study with 630 COVID-19 patients from Isfahan, Iran. Competing risk methods are used to develop a death risk chart based on the following variables, which can simply be measured at hospital admission: sex, age, hypertension, oxygen saturation, and Charlson Comorbidity Index. The area under the receiver operator curve was used to assess accuracy concerning discrimination between patients discharged alive and dead. // Results: Cause-specific hazard regression models show that these baseline variables are associated with both death, and discharge hazards. The risk chart reflects the combined results of the two cause-specific hazard regression models. The proposed risk assessment method had a very good accuracy (AUC¿=¿0.872 [CI 95%: 0.835–0.910]). // Conclusions: This study aims to improve and validate a personalized mortality risk calculator based on hospitalized COVID-19 patients. The risk assessment of patient mortality provides physicians with additional guidance for making tough decisions., The research leading to these results has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 712949 (TECNIOspring PLUS), and from the Agency for Business Competitiveness of the Government of Catalonia (TECSPR18-1–0017). The TECSPR18-1–0017 project provided the APC. These funders had no role in study design, data collection, analysis, decision to publish, or manuscript preparation., Peer Reviewed, Postprint (published version)

Identifying the possible factors of psychiatric symptoms among children can reduce the risk of adverse psychosocial outcomes in adulthood. We designed a classification tool to examine the association between modifiable risk factors and psychiatric symptoms, defined based on the Persian version of the WHO-GSHS questionnaire in a developing country. Ten thousand three hundred fifty students, aged 6–18 years from all Iran provinces, participated in this study. We used feature discretization and encoding, stability selection, and regularized group method of data handling (GMDH) to classify the a priori specific factors (e.g., demographic, sleeping-time, life satisfaction, and birth-weight) to psychiatric symptoms. Self-rated health was the most critical feature. The selected modifiable factors were eating breakfast, screentime, salty snack for depression symptom, physical activity, salty snack for worriedness symptom, (abdominal) obesity, sweetened beverage, and sleep-hour for mild-to-moderate emotional symptoms. The area under the ROC curve of the GMDH was 0.75 (CI 95% 0.73–0.76) for the analyzed psychiatric symptoms using threefold cross-validation. It significantly outperformed the state-of-the-art (adjusted p¿<¿0.05; McNemar's test). In this study, the association of psychiatric risk factors and the importance of modifiable nutrition and lifestyle factors were emphasized. However, as a cross-sectional study, no causality can be inferred., The authors would like to thank the CASPIAN team working on this national project and all students and their families participating in this project. The research leading to these results has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 712949 (TECNIOspring PLUS) and from the Agency for Business Competitiveness of the Government of Catalonia (TECSPR18-1-0017)., Peer Reviewed, Postprint (published version)

It is necessary to decompose the intra-muscular EMG signal to extract motor unit action potential (MUAP) waveforms and firing times. Some algorithms were proposed in the literature to resolve superimposed MUAPs, including Peel-Off (PO), branch and bound (BB), genetic algorithm (GA), and particle swarm optimization (PSO). This study aimed to compare these algorithms in terms of overall accuracy and running time. Two sets of two-to-five MUAP templates (set1: a wide range of energies, and set2: a high degree of similarity) were used. Such templates were time-shifted, and white Gaussian noise was added. A total of 1000 superpositions were simulated for each template and were resolved using PO (also, POI: interpolated PO), BB, GA, and PSO algorithms. The generalized estimating equation was used to identify which method significantly outperformed, while the overall rank product was used for overall ranking. The rankings were PSO, BB, GA, PO, and POI in the first, and BB, PSO, GA, PO, POI in the second set. The overall ranking was BB, PSO, GA, PO, and POI in the entire dataset. Although the BB algorithm is generally fast, there are cases where the BB algorithm is too slow and it is thus not suitable for real-time applications., This work was supported by the Spanish Ministry of Economy and Competitiveness - Spain (DPI2017-83989-R)., Peer Reviewed, Postprint (author's final draft)

Background: Developing simplified risk assessment model based on non-laboratory risk factors that could determine cardiovascular risk as accurately as laboratory-based one can be valuable, particularly in developing countries where there are limited resources. Objective: To develop a simplified non-laboratory cardiovascular disease risk assessment chart based on previously reported laboratory-based chart and evaluate internal and external validation, and recalibration of both risk models to assess the performance of risk scoring tools in other population. Methods: A 10-year non-laboratory-based risk prediction chart was developed for fatal and non-fatal CVD using Cox Proportional Hazard regression. Data from the Isfahan Cohort Study (ICS), a population-based study among 6504 adults aged = 35 years, followed-up for at least ten years was used for the non-laboratory-based model derivation. Participants were followed up until the occurrence of CVD events. Tehran Lipid and Glucose Study (TLGS) data was used to evaluate the external validity of both non-laboratory and laboratory risk assessment models in other populations rather than one used in the model derivation. Results: The discrimination and calibration analysis of the non-laboratory model showed the following values of Harrell’s C: 0.73 (95% CI 0.71–0.74), and Nam-D’Agostino ¿2:11.01 (p = 0.27), respectively. The non-laboratory model was in agreement and classified high risk and low risk patients as accurately as the laboratory one. Both non-laboratory and laboratory risk prediction models showed good discrimination in the external validation, with Harrell’s C of 0.77 (95% CI 0.75–0.78) and 0.78 (95% CI 0.76–0.79), respectively. Conclusions: Our simplified risk assessment model based on non-laboratory risk factors could determine cardiovascular risk as accurately as laboratory-based one. This approach can provide simple risk assessment tool where laboratory testing is unavailable, inconvenient, and costly., The baseline survey was supported by the undersecretary of research of the ministry of health and Isfahan cardiovascular research center, cardiovascular research institute affiliated to Isfahan University of Medical Sciences [grant number 31309304]. This work was also supported by the People Programme (Marie Curie Actions) of the European Union Seventh Framework Programme (FP7/2007–2013) [grant number 600388] and from the Agency for Business Competitiveness of the Government of Catalonia, ACC1Ó., Peer Reviewed, Postprint (published version)

Amputations can result in disability, permanent physical injury, and even posttraumatic stress disorder. Upper extremity amputations are mostly work-related, and such injuries include about 7% of the total burden of disease. High-functional artificial limbs are not available to most amputees because of their high price and the lack of public health coverage. Thus, there has been a significant interest in the design and fabrication of low-cost active upper-limb prostheses. Such devices are usually controlled by surface electromyographic (sEMG) signals. Recently, portable, low-cost recording devices such as Thalmic Labs Myo Gesture Control Armband have been used in the movement detection. Such devices use undersampled sEMG signals. In this chapter, we discuss upper-limb prostheses and their control. We further provide the results of some experiments showing that such undersampled signals could be used for various applications required in advanced prosthesis control, e.g., force prediction, elbow angle prediction, movement detection, and time and frequency parameter extraction using undersampled sEMG signals. Finally, a low-cost controller for BRUNEL HAND 2.0 from Open Bionics is designed to link low-cost recording and prosthesis., This work was supported by the Ministry of Economy and Competitiveness (MINECO), Spain, under contract DPI2017-83989-R and the Ministry of Science and Innovation (MICINN), Spain, under contract PRE2018-085387. CIBER-BBN is an initiative of the Instituto de Salud Carlos III, Spain. JF Alonso is a Serra Hunter Fellow. The research leading to this results has also received funding from the European's Union Horizon 2020 research and innovation program under the Marie Slodowska-Curie Grant Agreement No 712349 (TECNIOspring PLUS) and from the Agency for Business Competitiveness of the Government of Catalonia., Postprint (published version)

Coronavirus disease-2019, also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was a disaster in 2020. Accurate and early diagnosis of coronavirus disease-2019 (COVID-19) is still essential for health policymaking. Reverse transcriptase-polymerase chain reaction (RT-PCR) has been performed as the operational gold standard for COVID-19 diagnosis. We aimed to design and implement a reliable COVID-19 diagnosis method to provide the risk of infection using demographics, symptoms and signs, blood markers, and family history of diseases to have excellent agreement with the results obtained by the RT-PCR and CT-scan. Our study primarily used sample data from a 1-year hospital-based prospective COVID-19 open-cohort, the Khorshid COVID Cohort (KCC) study. A sample of 634 patients with COVID-19 and 118 patients with pneumonia with similar characteristics whose RT-PCR and chest CT scan were negative (as the control group) (dataset 1) was used to design the system and for internal validation. Two other online datasets, namely, some symptoms (dataset 2) and blood tests (dataset 3), were also analyzed. A combination of one-hot encoding, stability feature selection, over-sampling, and an ensemble classifier was used. Ten-fold stratified cross-validation was performed. In addition to gender and symptom duration, signs and symptoms, blood biomarkers, and comorbidities were selected. Performance indices of the cross-validated confusion matrix for dataset 1 were as follows: sensitivity of 96% [confidence interval, CI, 95%: 94–98], specificity of 95% [90–99], positive predictive value (PPV) of 99% [98–100], negative predictive value (NPV) of 82% [76–89], diagnostic odds ratio (DOR) of 496 [198–1,245], area under the ROC (AUC) of 0.96 [0.94–0.97], Matthews Correlation Coefficient (MCC) of 0.87 [0.85–0.88], accuracy of 96% [94–98], and Cohen's Kappa of 0.86 [0.81–0.91]. The proposed algorithm showed excellent diagnosis accuracy and class-labeling agreement, and fai, Peer Reviewed, Postprint (published version)

Background: Knowledge regarding neuropsychological training in Rett syndrome (RS) is scarce. The aim of this study was to assess the outcome and the duration of the effect of cognitive stimulation on topographic EEG data in RS. Method: Twenty female children diagnosed with RS were included in the analysis. Girls with RS conducted a cognitive task using an eye-tracker designed to evaluate access and choice skills. EEG data was acquired during the experimental procedure including two 10-minutes baseline stages before and after the task. Topographical changes of several EEG spectral markers including absolute and relative powers, brain symmetry index and entropy were assessed. Results: Topographic significance probability maps suggested statistical decreases on delta activity and increases on beta rhythm associated with the cognitive task. Entropy increased during and after the task, likely related to more complex brain activity. A significant positive interaction was obtained between brain symmetry index (BSI) and age showing that the improvement of interhemispheric symmetry was higher in younger girls (5-10 years). Conclusions: According to our findings, significant alterations of brain rhythms were observed during and after cognitive stimulation, suggesting that cognitive stimulation may have effects on brain activity beyond the stimulation period. Finally, our promising results also showed an increased brain symmetry that was especially relevant for the younger group. This could suggest an interaction of the eye-tracking cognitive task, however, further studies in this field are needed to assess the relation between brain asymmetries and age., We would like to acknowledge specific funding support from the Spanish Patient Associations Mi Princesa Rett and Rettando al Síndrome de Rett. This project has also received funding from Torrons Vicens and the Ministry of Science and Innovation (MICINN), Spain, under contract PID2020-117751RB-I00. CIBER-BBN is an initiative of the Instituto de Salud Carlos III, Spain. A. Bachiller is a Serra Húnter Fellow. A.García-Cazorla is supported by FIS P118/00111 “Instituto de Salud Carlos III (ISCIII)” and “Fondo Europeo de desarrollo regional (FEDER)”. A. Tost has received the predoctoral scholarship FI-AGAUR from the Generalitat de Catalunya., Peer Reviewed, Postprint (author's final draft)

Rett syndrome is a disease that involves acute cognitive impairment and, consequently, a complex and varied symptomatology. This study evaluates the EEG signals of twenty-nine patients and classify them according to the level of movement artifact. The main goal is to achieve an artifact rejection strategy that performs well in all signals, regardless of the artifact level. Two different methods have been studied: one based on the data distribution and the other based on the energy function, with entropy as its main component. The method based on the data distribution shows poor performance with signals containing high amplitude outliers. On the contrary, the method based on the energy function is more robust to outliers. As it does not depend on the data distribution, it is not affected by artifactual events. A double rejection strategy has been chosen, first on a motion signal (accelerometer or EEG low-pass filtered between 1 and 10 Hz) and then on the EEG signal. The results showed a higher performance when working combining both artifact rejection methods. The energy-based method, to isolate motion artifacts, and the data-distribution-based method, to eliminate the remaining lower amplitude artifacts were used. In conclusion, a new method that proves to be robust for all types of signals is designed., We would like to acknowledge specific funding support from the Spanish Patient Associations Mi Princesa Rett and Rettando al Síndrome de Rett. This project has also received funding from Torrons Vicens and the Ministry of Economy and Competitiveness (MINECO), Spain, under contract DPI2017-83989-R. CIBER-BBN is an initiative of the Instituto de Salud Carlos III, Spain. Alejandro Bachiller is a Serra Húnter Fellow. A.G.C. is supported by FIS P118/00111 “Instituto de Salud Carlos III (ISCIII)” and “Fondo Europeo de desarrollo regional (FEDER)”. Ana Tost has received the predoctoral scholarship FI-AGAUR from the Generalitat de Catalunya., Peer Reviewed, Postprint (published version)

The 2019 novel coronavirus disease (COVID-19) epidemic was officially announced by the World Health Organization (WHO) as an international public health emergency. The medical research world is responding to the COVID-19 pandemic at breathtaking speed. Most of the studies related to this outbreak identify the epidemiology and clinical characteristics of infected patients and focus on its short-term effects. However, there are many studies with inappropriate study design, data mining, and statistical analysis. Proper design and reliability assessment of COVID-19 diagnosis systems (e.g., proper feature selection, classification, and performance assessment) must be performed. Also, advanced statistical methods (e.g., multistate and competing risk models) are required to avoid the risk of bias in prognosis systems. Moreover, many studies may be too small and poorly designed to be helpful, merely adding to the COVID-19 noise. Additionally, trials without a control group, non-randomized and imbalanced trials are common problems of experimental designs. Thus, in this chapter, we aim to address the critical methods to use in the diagnosis and prognosis of COVID-19., The research leading to this results has also received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement Nº 712949 (TECNIOspring PLUS) and from the Agency for Business Competitiveness of the Government of Catalonia., Peer Reviewed, Postprint (published version)

The electrical activity generated in contracting muscles is measured using electromyographic (EMG) signals. By placing an array of electrodes on the surface of the skin, surface EMG (sEMG) signals are recorded non-invasively. The sEMG signal is a stochastic signal whose amplitude is generally between 1 and 10 mV, with the most dominant spectral power between 50 and 150 Hz. The sEMG signal is often corrupted by various types of noise, such as movement artifacts, power-line interference and activity from the other muscles. Moreover, the electrode placement could affect the recorded signals. They make analysing and classifying sEMG signals difficult. sEMG has applications in rehabilitation, sport science, kinesiology, ergonomics, muscle architecture identification, neurological disease diagnosis, prosthesis control and human–machine interfaces. In this chapter, the sEMG signal processing methods used in such applications are discussed, and critical issues are considered. Finally, a basic prosthesis control example is provided for interested readers., This work was supported by the Ministry of Economy and Competitiveness (MINECO), Spain, under contract DPI2017-83989-R and Ministry of Science and Innovation (MICINN), Spain, under contract PRE2018-085387. CIBER-BBN is an initiative of the Instituto de Salud Carlos III, Spain. JFA is a Serra Hunter Fellow. This work was also supported in part by the University of Isfahan., Peer Reviewed, Postprint (published version)

Background: The aim of this study was to determine whether computer-aided training (CAT) of motor tasks would increase muscle activity and change its spatial distribution in a patient with a bilateral upper-limb congenital transverse deficiency. We believe that our study makes a significant contribution to the literature because it demonstrates the usefulness of CAT in promoting the neuromuscular adaptation in people with congenital limb deficiencies and altered body image. Case presentation: The patient with bilateral upper-limb congenital transverse deficiency and the healthy control subject performed 12 weeks of the CAT. The subject’s task was to imagine reaching and grasping a book with the hand. Subjects were provided a visual animation of that movement and sensory feedback to facilitate the mental engagement to accomplish the task. High-density electromyography (HD-EMG; 64-electrode) were collected from the trapezius muscle during a shrug isometric contraction before and after 4, 8, 12 weeks of the training. After training, we observed in our patient changes in the spatial distribution of the activation, and the increased average intensity of the EMG maps and maximal force. Conclusions: These results, although from only one patient, suggest that mental training supported by computergenerated visual and sensory stimuli leads to beneficial changes in muscle strength and activity. The increased muscle activation and changed spatial distribution of the EMG activity after mental training may indicate the training-induced functional plasticity of the motor activation strategy within the trapezius muscle in individual with bilateral upper-limb congenital transverse deficiency. Marked changes in spatial distribution during the submaximal contraction in the patient after training could be associated with changes of the neural drive to the muscle, which corresponds with specific (unfamiliar for patient) motor task. These findings are relevant to neuromuscular functional, Peer Reviewed, Postprint (published version)

Rett syndrome is a disease that involves acute cognitive impairment and, consequently, a complex and varied symptomatology. This study evaluates the EEG signals of twenty-nine patients in order to implement an effective classification method to find the optimal artifact reduction strategy in each case. The classification has been made based on the mean and standard deviation (SD), allowing to differentiate patients with stereotyped and constant movements from those with a greater number of spasm or sudden movements. Since the various signal patterns may require diverse treatments, two artifact reduction methods have been analyzed. The first oneis based on the distribution, using again the mean and SD, and the second one is based on an energy function which, theoretically, should be more robust to outliers and more stable to signal to noise ratio. The results confirm the existence of three groups of signals differentiated by having: low mean and low SD, high mean and low SD and high mean and high SD. However, despite finding three different patterns, the energy-based method is the one that works best for all them, offering adequate adaptation to each type of signal without losing robustness and stability. In conclusion, its implementation improves the detection of outliers without compromising artifact-free data segments, which allows to maintain the quality and quantity of the records., We would like to acknowledge specific funding support from the Spanish Patient Associations Mi Princesa Rett and Rettando al Síndrome de Rett. This project has receivedalso funding from Torrons Vicens and the Ministry of Economy and Competitiveness (MINECO), Spain, under contract DPI2017-83989-R. CIBER-BBN is an initiative of the Instituto de Salud Carlos III, Spain. Alejandro Bachiller is a Serra Húnter Fellow., Peer Reviewed, Postprint (published version)

BACKGROUND: A comprehensive study on the interaction of cardiovascular disease (CVD) risk factors is critical to prevent cardiovascular events. The main focus of this study is thus to understand direct and indirect relationships between different CVD risk factors. METHODS: A longitudinal data on adults aged =35¿years, who were free of CVD at baseline, were used in this study. The endpoints were CVD events, whereas their measurements were demographic, lifestyle components, socio-economics, anthropometric measures, laboratory findings, quality of life status, and psychological factors. A Bayesian structural equation modelling was used to determine the relationships among 21 relevant factors associated with total CVD, stroke, acute coronary syndrome (ACS), and fatal CVDs. RESULTS: In this study, a total of 3161 individuals with complete information were involved in the study. A total of 407 CVD events, with an average age of 54.77(10.66) years, occurred during follow-up. The causal associations between six latent variables were identified in the causal network for fatal and non-fatal CVDs. Lipid profile, with the coefficient of 0.26 (0.01), influenced the occurrence of CVD events as the most critical factor, while it was indirectly mediated through risky behaviours and comorbidities. Lipid profile at baseline was influenced by a wide range of other protective factors, such as quality of life and healthy lifestyle components. CONCLUSIONS: Analysing a causal network of risk factors revealed the flow of information in direct and indirect paths. It also determined predictors and demonstrated the utility of integrating multi-factor data in a complex framework to identify novel preventable pathways to reduce the risk of CVDs., Postprint (published version)

In this chapter, we discuss how intracranial or scalp electroencephalographic and magnetoencephalographic recordings could be used for epileptic seizure prediction and onset zone localization. The signal processing methods and the challenges of the state-of-the-art are discussed., Peer Reviewed, Postprint (published version)

The COVID-19 is rapidly scattering worldwide, and the number of cases in the Eastern Mediterranean Region is rising. Thus, there is a need for immediate targeted actions. We designed a longitudinal study in a hot outbreak zone to analyze the serial findings between infected patients for detecting temporal changes from February 2020. In a hospital-based open-cohort study, patients are followed from admission until one year from their discharge (the 1st, 4th, 12th weeks, and the first year). The patient recruitment phase finished at the end of August 2020, and the follow-up continues by the end of August 2021. The measurements included demographic, socio-economics, symptoms, health service diagnosis and treatment, contact history, and psychological variables. The signs improvement, death, length of stay in hospital were considered primary, and impaired pulmonary function and psychotic disorders were considered main secondary outcomes. Moreover, clinical symptoms and respiratory functions are being determined in such follow-ups. Among the first 600 COVID-19 cases, 490 patients with complete information (39% female; the average age of 57±15 years) were analyzed. Seven percent of these patients died. The three main leading causes of admission were: fever (77%), dry cough (73%), and fatigue (69%). The most prevalent comorbidities between COVID-19 patients were hypertension (35%), diabetes (28%), and ischemic heart disease (14%). The percentage of primary composite endpoints (PCEP), defined as death, the use of mechanical ventilation, or admission to an intensive care unit was 18%. The Cox Proportional-Hazards Model for PCEP indicated the following significant risk factors: Oxygen saturation < 80% (HR = 6.3; [CI 95%: 2.5,15.5]), lymphopenia (HR = 3.5; [CI 95%: 2.2,5.5]), Oxygen saturation 80%-90% (HR = 2.5; [CI 95%: 1.1,5.8]), and thrombocytopenia (HR = 1.6; [CI 95%: 1.1,2.5]). This long-term prospective Cohort may support healthcare professionals in the management of reso, Peer Reviewed, Postprint (published version)

Objective. We propose a novel automated method called the S-Transform Gaussian Mixture detection algorithm (SGM) to detect high-frequency oscillations (HFO) combining the strengths of different families of previously published detectors. Approach. This algorithm does not depend on parameter tuning on a subject (or database) basis, uses time-frequency characteristics, and relies on non-supervised classification to determine if the events standing out from the baseline activity are HFO or not. SGM consists of three steps: the first stage computes the signal baseline using the entropy of the autocorrelation; the second uses the S-Transform to obtain several time-frequency features (area, entropy, and time and frequency widths); and in the third stage Gaussian mixture models cluster time-frequency features to decide if events correspond to HFO-like activity. To validate the SGM algorithm we tested its performance in simulated and real environments. Main results. We assessed the algorithm on a publicly available simulated stereoelectroencephalographic (SEEG) database with varying signal-to-noise ratios (SNR), obtaining very good results for medium and high SNR signals. We further tested the SGM algorithm on real signals from patients with focal epilepsy, in which HFO detection was performed visually by experts, yielding a high agreement between experts and SGM. Significance. The SGM algorithm displayed proper performance in simulated and real environments and therefore can be used for non-supervised detection of HFO. This non-supervised algorithm does not require previous labelling by experts or parameter adjustment depending on the subject or database considered. SGM is not a computationally intensive algorithm, making it suitable to detect and characterize HFO in long-term SEEG recordings., Peer Reviewed, Postprint (published version)

This paper presents a dataset of high-density surface EMG signals (HD-sEMG) designed to study patterns of sEMG spatial distribution over upper limb muscles during voluntary isometric contractions. Twelve healthy subjects performed four diferent isometric tasks at diferent efort levels associated with movements of the forearm. Three 2-D electrode arrays were used for recording the myoelectric activity from fve upper limb muscles: biceps brachii, triceps brachii, anconeus, brachioradialis, and pronator teres. Technical validation comprised a signals quality assessment from outlier detection algorithms based on supervised and non-supervised classifcation methods. About 6% of the total number of signals were identifed as “bad” channels demonstrating the high quality of the recordings. In addition, spatial and intensity features of HD-sEMG maps for identifcation of efort type and level, have been formulated in the framework of this database, demonstrating better performance than the traditional time-domain features. The presented database can be used for pattern recognition and MUAP identifcation among other uses., Peer Reviewed, Postprint (published version)

Postprint (author's final draft)

Postprint (author's final draft)