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Objective: Wearable nonelectroencephalographic biosignal recordings captured from the wrist offer enormous potential for seizure monitoring. However, signal quality remains a challenging factor affecting data reliability. Models trained for seizure detection depend on the quality of recordings in peri‐ictal periods in performing a feature‐based separation of ictal periods from interictal periods. Thus, this study aims to investigate the effect of epileptic seizures on signal quality, ensuring accurate and reliable monitoring. Methods: This study assesses the signal quality of wearable data during peri‐ictal phases of generalized tonic–clonic and focal to bilateral tonic–clonic seizures (TCS), focal motor seizures (FMS), and focal nonmotor seizures (FNMS). We evaluated accelerometer (ACC) activity and the signal quality of electrodermal activity (EDA) and blood volume pulse (BVP) data. Additionally, we analyzed the influence of peri‐ictal movements as assessed by ACC (ACC activity) on signal quality and examined intraictal subphases of focal to bilateral TCS. Results: We analyzed 386 seizures from 111 individuals in three international epilepsy monitoring units. BVP signal quality and ACC activity levels differed between all seizure types. We found the largest decrease in BVP signal quality and increase in ACC activity when comparing the ictal phase to the pre‐ and postictal phases for TCS. Additionally, ACC activity was strongly negatively correlated with BVP signal quality for TCS and FMS, and weakly for FNMS. Intraictal analysis revealed that tonic and clonic subphases have the lowest BVP signal quality and the highest ACC activity. Significance: Motor elements of seizures significantly impair BVP signal quality, but do not have significant effect on EDA signal quality, as assessed by wrist‐worn wearables. The results underscore the importance of signal quality assessment methods and careful selection of robust modalities to ensure reliable seizure detection. Future research is needed to explain whether seizure detection models' decisions are based on signal responses induced by physiological processes as opposed to artifacts. [ABSTRACT FROM AUTHOR]

Induction-based breathing sensors in automobiles enable unobtrusive respiratory rate monitoring as an indicator of a driver's alertness and health. This paper introduces a quantitative method based on signal quality to guide the integration of textile inductive electrodes in automotive applications. A case study with a simplified setup illustrated the ability of the method to successfully provide basic design rules about where and how to integrate the electrodes on seat belts and seat backs to gather good quality respiratory signals in an automobile. The best signals came from the subject's waist, then from the chest, then from the upper back, and finally from the lower back. Furthermore, folding the electrodes before their integration on a seat back improves the signal quality for both the upper and lower back. This analysis provided guidelines with three design rules to increase the chance of acquiring good quality signals: (1) use a multi-electrode acquisition approach, (2) place the electrodes in locations that maximize breathing-induced body displacement, and (3) use a mechanical amplifying method such as folding the electrodes in locations with little potential for breathing-induced displacement. [ABSTRACT FROM AUTHOR]

The quality of remote photoplethysmography (rPPG) signals presents a significant challenge in contactless optical blood pressure measurement. Feature and morphologybased approaches heavily rely on subtle changes in signal characteristics, but rPPG signals are highly susceptible to noise and interference. This study aims to evaluate rPPG signal quality by assessing correlation with a reference PPG and signal-to-noise ratio (SNR) across various color models and rPPG methods. Analyses are performed under different measurement conditions, accounting for common sources of signal artifacts. [ABSTRACT FROM AUTHOR]

With the growing popularity of consumer-grade electroencephalogram (EEG) devices for health, entertainment, and cognitive research, assessing their signal quality is essential. In this study, we evaluated four consumer-grade wireless and dry-electrode EEG systems widely used for brain-computer interface (BCI) research and applications, comparing them with a research-grade system. We designed an EEG phantom method that reproduced µV-level amplitude EEG signals and evaluated the five devices based on their spectral responses, temporal patterns of event-related potential (ERP), and spectral patterns of resting-state EEG. We discovered that the consumer-grade devices had limited bandwidth compared with the research-grade device. A late component (e.g., P300) was detectable in the consumer-grade devices, but the overall ERP temporal pattern was distorted. Only one device showed an ERP temporal pattern comparable to that of the research-grade device. On the other hand, we confirmed that the activation of the alpha rhythm was observable in all devices. The results provide valuable insights for researchers and developers when it comes to selecting suitable EEG devices for BCI research and applications. [ABSTRACT FROM AUTHOR]

One communication medium that is well-known for its outstanding and reliable performance is fiber optic. A social example of its application is the Fiber to the Home (FTTH) network. The goal of this study is to evaluate the signal quality of the customer base method and the branching method, two FTTH-building techniques based on the PT.PLN Icon Plus standards, in order to identify the most practical approach for use in the Air Hitam 2 cluster. Two scenarios were used in this study at the Fiber Access Terminal (FAT) with 1:16 and 1:8 splitters. The fiber optic cable path design findings demonstrate that the branching approach is a wise decision, utilizing optical fiber cables for a total of 9 Km, with the greatest cable distance being 2.5 Km from the Optical Line Terminal (OLT) to the end FAT. According to theory, in the 1:16 splitter situation and the 1:8 splitter scenario, the optical power received by the Optical Network Terminal (ONT) is -19.13 dBm and -16.03 dBm, respectively, with an OLT transmit power of 3 dBm. For these cases, the simulation results are -17.98 dBm and -20.27 dBm. Additionally, the budget value for the rising time reaches 0.253 ns. The bit error rate values in the 1:16 and 1:8 splitter scenarios are 3.157 × 10-10, and 1.63507 × 10-28, respectively, while the Q factor values are 6.18233 and 11.014, respectively. Based on theory and simulation, these findings suggest that the branching strategy can deliver good performance.

The Electro Cardio Gram is a very valuable clinical tool to access the electric function of the heart. It provides insight into the different phases of the heart beat and various kinds of disorders which may affect them. In the past decades various algorithms to detect and delineate ECG beats have been developed. The complex-lead based algorithms compute a virtual-lead by averaging the samples of an arbitrary number of channels. In this study exponential weighted moving powers based estimates for the skewness, kurtosis and signal to noise ratio are used to select the channels which are included in the resulting complex lead signal. According to these parameters the channels are labelled bad, poor, scaled and good. A weighted average is used to to compute the next sample of the complex lead from the channels labelled good and scaled. The 75 30minutes records of the ”St.-Petersburg Institute of Cardiological Technics 12-lead Arrhythmia Database” are used to evaluate the proposed approach. The average sensitivity increases to 99.61% and to 99.63% in total compared to 99.57% and 99.59% for the standard approach. The average and overall specificity increases to 99.65% and the F1 and F-M scores to 0.9964. This shows that the estimates provide sufficient information about the quality, to successfully reduce the distortion of the complex-lead signal by artefacts and strong muscle signals while avoiding the additional time delay imposed by standard window based methods.

We statistically study 10 weak to moderate geomagnetic storms that occurred since the 25th solar activity cycle to evaluate the quality of satellite signal and positioning accuracy of Beidou Navigation Satellite System (BDS). The results show that the PAS (Percentage of Affected Satellites) index, which integrates three scenarios: satellite signal loss, satellite signal loss of lock, and carrier phase signal anomaly, appears significant anomaly changes during weak and moderate geomagnetic storms, and its changing trend is closely related to the geomagnetic storm Kp index and Dst index, the correlation coefficient is greater than 0.75. Based on the results of PAS signal quality evaluation, the effect of PAS on the positioning error of Precision Point Positioning (PPP) of BDS-2/BDS-3 systems is analyzed and verified in conjunction with PAS. The results show that there is a strong agreement between the time of PAS anomaly and the time of PPP positioning accuracy anomaly. The positioning error of PPP increases by 9 to 44 times compared to the quiet period. The statistical results indicate that the PAS index is useful for the signal quality evaluation of the Beidou Navigation Satellite System under different space weather conditions. [ABSTRACT FROM AUTHOR]

Introduction: Wearable in-ear electroencephalographic (EEG) devices hold significant promise for integrating brain monitoring technologies into real-life applications. However, despite the introduction of various in-ear EEG systems, there remains a necessity for validating these technologies against goldstandard, clinical-grade devices. This study aims to evaluate the signal quality of a newly developed mobile in-ear EEG device compared to a standard scalp EEG system among healthy volunteers during wakefulness and sleep. Methods: The study evaluated an in-ear EEG device equipped with dry electrodes in a laboratory setting, recording a single bipolar EEG channel using a cross-ear electrode configuration. Thirty healthy participants were recorded simultaneously using the in-ear EEG device and a conventional EEG cap system with 64 wet electrodes. Based on two recording protocols, one during a resting state condition involving alternating eye opening and closure with a low degree of artifact contamination and another consisting of a daytime nap, several quality measures were used for a quantitative comparison including root mean square (RMS) analysis, artifact quantification, similarities of relative spectral power (RSP), signal-to-noise ratio (SNR) based on alpha peak criteria, and cross-signal correlations of alpha activity during eyes-closed conditions and sleep activities. The statistical significance of our results was assessed through nonparametric permutation tests with False Discovery Rate (FDR) control. Results: During the resting state, in-ear and scalp EEG signals exhibited similar fluctuations, characterized by comparable RMS values. However, intermittent signal alterations were noticed in the in-ear recordings during nap sessions, attributed to movements of the head and facial muscles. Spectral analysis indicated similar patterns between in-ear and scalp EEG, showing prominent peaks in the alpha range (8-12 Hz) during rest and in the low-frequency range during naps (particularly in the theta range of 4-7 Hz). Analysis of alpha wave characteristics during eye closures revealed smaller alpha wave amplitudes and slightly lower signal-to-noise ratio (SNR) values in the in-ear EEG compared to scalp EEG. In around 80% of cases, cross-correlation analysis between in-ear and scalp signals, using a contralateral bipolar montage of 64 scalp electrodes, revealed significant correlations with scalp EEG (p < 0.01), particularly evident in the FT11-FT12 and T7-T8 electrode derivations. Conclusion: Our findings support the feasibility of using in-ear EEG devices with dry-contact electrodes for brain activity monitoring, compared to a standard scalp EEG, notably for wakefulness and sleep uses. Although marginal signal degradation is associated with head and facial muscle contractions, the in-ear device offers promising applications for long-term EEG recordings, particularly in scenarios requiring enhanced comfort and user-friendliness. [ABSTRACT FROM AUTHOR]

Bilirubin is a product of the metabolism of hemoglobin from red blood cells. Higher levels of bilirubin are a sign that either there is an unusual breaking down rate of red blood cells or the liver is not able to eliminate bilirubin, through bile, into the gastrointestinal tract. For adults, bilirubin is occasionally monitored through urine or invasive blood sampling, whilst all newborns are routinely monitored visually, or non-invasively with transcutaneous measurements (TcBs), due to their biological immaturity to conjugate bilirubin. Neonatal jaundice is a common condition, with higher levels of unconjugated bilirubin concentration having neurotoxic effects. Actual devices used in TcBs are focused on newborn populations, are hand-held, and, in some cases, operate in only two wavelengths, which does not necessarily guarantee reliable results over all skin tones. The same occurs with visual inspections. Based on that, a continuous bilirubin monitoring device for newborns is being developed to overcome visual inspection errors and to reduce invasive procedures. This device, operating optically with a mini-spectrometer in the visible range, is susceptible to patient movements and, consequently, to situations with a lower signal quality for reliable bilirubin concentration estimates on different types of skin. Therefore, as an intermediate development step and, based on skin spectra measurements from adults, this work addresses the device's placement status prediction as a signal quality indication index. This was implemented by using machine learning (ML), with the best performances being achieved by support vector machine (SVM) models, based on the spectra acquired on the arm and forehead areas. [ABSTRACT FROM AUTHOR]

Wearable in-ear electroencephalographic (EEG) devices hold significant promise for advancing brain monitoring technologies into everyday applications. However, despite the current availability of several in-ear EEG devices in the market, there remains a critical need for robust validation against established clinical-grade systems. In this study, we carried out a detailed examination of the signal performance of a mobile in-ear EEG device from Naox Technologies. Our investigation had two main goals: firstly, evaluating the hardware circuit's reliability through simulated EEG signal experiments and, secondly, conducting a thorough comparison between the in-ear EEG device and gold-standard EEG monitoring equipment. This comparison assesses correlation coefficients with recognized physiological patterns during wakefulness and sleep, including alpha rhythms, eye artifacts, slow waves, spindles, and sleep stages. Our findings support the feasibility of using this in-ear EEG device for brain activity monitoring, particularly in scenarios requiring enhanced comfort and user-friendliness in various clinical and research settings. [ABSTRACT FROM AUTHOR]

The 5G wireless standard has emerged as a trans-formative technology with the potential to revolutionize various industries by providing enhanced connectivity and communication capabilities. This advanced standard offers a diverse range of applications, including Ultra-Reliable Low-Latency Communication (URLLC), Enhanced Mobile Broadband (eMBB), and Massive Machine Type Communication (mMTC). In this Scientific research paper, we present a comprehensive analysis of the performance and capabilities of a deployed indoor 5G network in a controlled laboratory environment. The experimental setup comprises an Amarisoft Callbox, serving as the 5G core, along with a Remote Radio Head (RRH) and user equipment (UEs). Our primary objective is to assess the network's performance by evaluating the downlink, uplink, and joint downlink/uplink transmissions for TCP and UDP protocols between the gNodeB and user equipment (UE). To achieve this, we carefully examine key performance metrics such as latency, power consumption, CPU utilization, and signal quality. Through various configurations that involve MIMO 2 technology and a 30 kHz sub-carrier spacing, we investigate the impact of different NR Bandwidth settings. By establishing TCP and UDP connections for both uplink and downlink scenarios, we meticulously measure and scrutinize the system's performance, thereby providing valuable insights into its efficiency and suitability for diverse application requirements. [ABSTRACT FROM AUTHOR]

The expansion of the Internet of Things (IoT) has created a need for reliable and fault-tolerant communication networks. However, ensuring consistent signal quality and power efficiency has proven to be challenging. This study evaluated the performance of Narrowband IoT (NB-IoT) communications using the SIM7020 module connected to a Raspberry Pi 4 Model B, focusing on signal quality across indoor, outdoor, urban and rural areas. Supervised machine learning for indoor localisation based on Received Signal Strength Indicator (RSSI) has been introduced, for example, to enhance NB-IoT performance. However, this and other approaches have encountered difficulties in mobile and obstructed environments, including signal attenuation, connectivity variability and increased power consumption. The objective of this study was to analyse NB-IoT signal strength and power consumption, providing guidance for deploying real-time communication IoT applications. Empirical data was analysed to understand the RSSI and Cellular Signal Quality (CSQ) in different locations. Signal quality in urban and outdoor environments was prone to fluctuations due to mobility and interference, whereas rural areas had weaker but more consistent signals. Indoor environments suffered from significant signal attenuation. This study's results emphasise the importance of improved handover mechanisms and adaptive deployment strategies to ensure reliable connectivity across various IoT applications. [ABSTRACT FROM AUTHOR]

Wearable electronics are increasingly common and useful as health monitoring devices, many of which feature the ability to record a single-lead electrocardiogram (ECG). However, recording the ECG commonly requires the user to touch the device to complete the lead circuit, which prevents continuous data acquisition. An alternative approach to enable continuous monitoring without user initiation is to embed the leads in a garment. This study assessed ECG data obtained from the YouCare device (a novel sensorized garment) via comparison with a conventional Holter monitor. A cohort of thirty patients (age range: 20–82 years; 16 females and 14 males) were enrolled and monitored for twenty-four hours with both the YouCare device and a Holter monitor. ECG data from both devices were qualitatively assessed by a panel of three expert cardiologists and quantitatively analyzed using specialized software. Patients also responded to a survey about the comfort of the YouCare device as compared to the Holter monitor. The YouCare device was assessed to have 70% of its ECG signals as "Good", 12% as "Acceptable", and 18% as "Not Readable". The R-wave, independently recorded by the YouCare device and Holter monitor, were synchronized within measurement error during 99.4% of cardiac cycles. In addition, patients found the YouCare device more comfortable than the Holter monitor (comfortable 22 vs. 5 and uncomfortable 1 vs. 18, respectively). Therefore, the quality of ECG data collected from the garment-based device was comparable to a Holter monitor when the signal was sufficiently acquired, and the garment was also comfortable. [ABSTRACT FROM AUTHOR]

Smart wearables support continuous monitoring of vital signs for early detection of deteriorating health. However, the devices and sensors require sufficient quality to produce meaningful signals, in particular, if data is acquired in motion. In this study, we equipped 48 subjects with smart shirts recording one-lead electrocardiography (ECG), thoracic and abdominal respiratory inductance plethysmography, and three-axis acceleration. For 10 min each, the subjects sit, stand, walk, and run, with a resting period of 5 min in between each activity. We preprocessed the electrocardiogram and applied a signal quality index. We analyzed the signal quality index grouped by the activity and participants. For sitting, standing, walking, and running, the ECG signals provide acceptable quality over 73.20 %, 91.85 %, 12.26 %, and 13.14 % of the recording time. In conclusion, smart wearables may be useful for continuous health monitoring of people with a sedentary lifestyle, but rather not for sportive activities. [ABSTRACT FROM AUTHOR]

IntroductionWearable in-ear electroencephalographic (EEG) devices hold significant promise for integrating brain monitoring technologies into real-life applications. However, despite the introduction of various in-ear EEG systems, there remains a necessity for validating these technologies against gold-standard, clinical-grade devices. This study aims to evaluate the signal quality of a newly developed mobile in-ear EEG device compared to a standard scalp EEG system among healthy volunteers during wakefulness and sleep.MethodsThe study evaluated an in-ear EEG device equipped with dry electrodes in a laboratory setting, recording a single bipolar EEG channel using a cross-ear electrode configuration. Thirty healthy participants were recorded simultaneously using the in-ear EEG device and a conventional EEG cap system with 64 wet electrodes. Based on two recording protocols, one during a resting state condition involving alternating eye opening and closure with a low degree of artifact contamination and another consisting of a daytime nap, several quality measures were used for a quantitative comparison including root mean square (RMS) analysis, artifact quantification, similarities of relative spectral power (RSP), signal-to-noise ratio (SNR) based on alpha peak criteria, and cross-signal correlations of alpha activity during eyes-closed conditions and sleep activities. The statistical significance of our results was assessed through nonparametric permutation tests with False Discovery Rate (FDR) control.ResultsDuring the resting state, in-ear and scalp EEG signals exhibited similar fluctuations, characterized by comparable RMS values. However, intermittent signal alterations were noticed in the in-ear recordings during nap sessions, attributed to movements of the head and facial muscles. Spectral analysis indicated similar patterns between in-ear and scalp EEG, showing prominent peaks in the alpha range (8–12 Hz) during rest and in the low-frequency range during naps (particularly in the theta range of 4–7 Hz). Analysis of alpha wave characteristics during eye closures revealed smaller alpha wave amplitudes and slightly lower signal-to-noise ratio (SNR) values in the in-ear EEG compared to scalp EEG. In around 80% of cases, cross-correlation analysis between in-ear and scalp signals, using a contralateral bipolar montage of 64 scalp electrodes, revealed significant correlations with scalp EEG (p

Induction-based breathing sensors in automobiles enable unobtrusive respiratory rate monitoring as an indicator of a driver’s alertness and health. This paper introduces a quantitative method based on signal quality to guide the integration of textile inductive electrodes in automotive applications. A case study with a simplified setup illustrated the ability of the method to successfully provide basic design rules about where and how to integrate the electrodes on seat belts and seat backs to gather good quality respiratory signals in an automobile. The best signals came from the subject’s waist, then from the chest, then from the upper back, and finally from the lower back. Furthermore, folding the electrodes before their integration on a seat back improves the signal quality for both the upper and lower back. This analysis provided guidelines with three design rules to increase the chance of acquiring good quality signals: (1) use a multi-electrode acquisition approach, (2) place the electrodes in locations that maximize breathing-induced body displacement, and (3) use a mechanical amplifying method such as folding the electrodes in locations with little potential for breathing-induced displacement.

Introduction: Recordings of electroencephalographic (EEG) rhythms and their analyses have been instrumental in basic neuroscience, clinical diagnostics, and the field of brain-computer interfaces (BCIs). While in the past such measurements have been conducted mostly in laboratory settings, recent advancements in dry electrode technology pave way to a broader range of consumer and medical application because of their greater convenience compared to gel-based electrodes. Methods: Here we conducted resting-state EEG recordings in two groups of healthy participants using three dry-electrode devices, the PSBD Headband, the PSBD Headphones and the Muse Headband, and one standard gel electrode- based system, the NVX. We examined signal quality for various spatial and spectral ranges which are essential for cognitive monitoring and consumer applications. Results: Distinctive characteristics of signal quality were found, with the PSBD Headband showing sensitivity in low-frequency ranges and replicating the modulations of delta, theta and alpha power corresponding to the eyes-open and eyes-closed conditions, and the NVX system performing well in capturing high-frequency oscillations. The PSBD Headphones were more prone to low- frequency artifacts compared to the PSBD Headband, yet recorded modulations in the alpha power and had a strong alignment with the NVX at the higher EEG frequencies. The Muse Headband had several limitations in signal quality. Discussion: We suggest that while dry-electrode technology appears to be appropriate for the EEG rhythm-based applications, the potential benefits of these technologies in terms of ease of use and accessibility should be carefully weighed against the capacity of each given system. [ABSTRACT FROM AUTHOR]

The purpose of this study is to investigate the factors affecting consumers' repurchase intentions of skincare cosmetics in Can Tho city. By examining consumers’ knowledge, behaviors, and factors impacting their intention of repeatedly buying skincare products, manufacturers and sales businesses are able to more effectively comprehend their clients' demands. Data were collected from 531 consumers residing in the area of Can Tho city, Vietnam, who had bought skincare products for the second time or more. The survey using the cross-sectional method and a set of questionnaires was based on the theory of planned behavior (TPB) and developed on the 5-point Likert scale. The factors impacting repurchase intentions for skincare merchandise involve attitude, reliability of signal quality, and retailer credibility, with attitude being the most powerful influence, followed by retailer credibility and reliability of signal quality respectively. These factors accounted for 27.5 % of behavior variance. The clarity element of signal quality reliability does not affect behavioral intention. Cosmetic companies can develop policies, improve the quality of their products in order to suit the customer needs, retain potential clients, and enhance their competitive capability.

Goal: Recently, large datasets of biosignals acquired during surgery became available. As they offer multiple physiological signals measured in parallel, multimodal analysis – which involves their joint analysis – can be conducted and could provide deeper insights than unimodal analysis based on a single signal. However, it is unclear what percentage of intraoperatively acquired data is suitable for multimodal analysis. Due to the large amount of data, manual inspection and labelling into suitable and unsuitable segments are not feasible. Nevertheless, multimodal analysis is performed successfully in sleep studies since many years as their signals have proven suitable. Hence, this study evaluates the suitability to perform multimodal analysis on a surgery dataset (VitalDB) using a multi-center sleep dataset (SIESTA) as reference. Methods: We applied widely known algorithms entitled “signal quality indicators” to the common biosignals in both datasets, namely electrocardiography, electroencephalography, and respiratory signals split in segments of 10 s duration. As there are no multimodal methods available, we used only unimodal signal quality indicators. In case, all three signals were determined as being adequate by the indicators, we assumed that the whole signal segment was suitable for multimodal analysis. Results: 82% of SIESTA and 72% of VitalDB are suitable for multimodal analysis. Unsuitable signal segments exhibit constant or physiologically unreasonable values. Histogram examination indicated similar signal quality distributions between the datasets, albeit with potential statistical biases due to different measurement setups. Conclusions: The majority of data within VitalDB is suitable for multimodal analysis.

Objective: Non-sustained supraventricular tachycardia (nsSVT) is associated with a higher risk of developing atrial fibrillation (AF), and, therefore, detection of nsSVT can improve AF screening efficiency. However, the detection is challenged by the lower signal quality of ECGs recorded using handheld devices and the presence of ectopic beats which may mimic the rhythm characteristics of nsSVT.Methods: The present study introduces a new nsSVT detector for use in single-lead, 30-s ECGs, based on the assumption that beats in an nsSVT episode exhibits similar morphology, implying that episodes with beats of deviating morphology, either due to ectopic beats or noise/artifacts, are excluded. A support vector machine is used to classify successive 5-beat sequences in a sliding window with respect to similar morphology. Due to the lack of adequate training data, the classifier is trained using simulated ECGs with varying signal-to-noise ratio. In a subsequent step, a set of rhythm criteria is applied to similar beat sequences to ensure that episode duration and heart rate is acceptable.Results: The performance of the proposed detector is evaluated using the StrokeStop II database, resulting in sensitivity, specificity, and positive predictive value of 84.6%, 99.4%, and 18.5%, respectively. Conclusion: The results show that a significant reduction in expert review burden (factor of 6) can be achieved using the proposed detector.Clinical and Translational Impact: The reduction in the expert review burden shows that nsSVT detection in AF screening can be made considerably more efficiently.

Objective: A patient-independent approach for continuous estimation of vital signs using robust spectro-temporal features derived from only photoplethysmogram (PPG) signal. Methods: In the pre-processing stage, we remove baseline shifts and artifacts of the PPG signal using Incremental Merge Segmentation with adaptive thresholding. From the cleaned PPG, we extract multiple parameters independent of individual patient PPG morphology for both Respiration Rate (RR) and Blood Pressure (BP). In addition, we derived a set of novel spectral and statistical features strongly correlated to BP. We proposed robust correlation-based feature selection methods for accurate RR estimates. For fewer computations and accurate measurements of BP, the most significant features are selected using correlation and mutual information measures in the feature engineering part. Finally, RR and BP are estimated using breath counting and a neural network regression model, respectively. Results: The proposed approach outperforms the current state-of-the-art in both RR and BP. The RR algorithm results in mean absolute errors (median, 25th-75th percentiles) of 0.4 (0.1–0.7) for CapnoBase dataset and 0.5(0.3-2.8) for BIDMC dataset without discarding any data window. Similarly, BP approach has been validated on a large dataset derived from MIMIC-II ($\sim$1700 records) which has errors (mean absolute, standard deviation) of 5.0(6.3) and 3.0(4.0) for systolic and diastolic BP, respectively. The results meet the American Association for the Advancement of Medical Instrumentation (AAMI) and British Hypertension Society (BHS) Class A criteria. Conclusion: By using robust features and feature selection methods, we alleviated patient dependency to have reliable estimates of vitals.

Bilirubin is a product of the metabolism of hemoglobin from red blood cells. Higher levels of bilirubin are a sign that either there is an unusual breaking down rate of red blood cells or the liver is not able to eliminate bilirubin, through bile, into the gastrointestinal tract. For adults, bilirubin is occasionally monitored through urine or invasive blood sampling, whilst all newborns are routinely monitored visually, or non-invasively with transcutaneous measurements (TcBs), due to their biological immaturity to conjugate bilirubin. Neonatal jaundice is a common condition, with higher levels of unconjugated bilirubin concentration having neurotoxic effects. Actual devices used in TcBs are focused on newborn populations, are hand-held, and, in some cases, operate in only two wavelengths, which does not necessarily guarantee reliable results over all skin tones. The same occurs with visual inspections. Based on that, a continuous bilirubin monitoring device for newborns is being developed to overcome visual inspection errors and to reduce invasive procedures. This device, operating optically with a mini-spectrometer in the visible range, is susceptible to patient movements and, consequently, to situations with a lower signal quality for reliable bilirubin concentration estimates on different types of skin. Therefore, as an intermediate development step and, based on skin spectra measurements from adults, this work addresses the device’s placement status prediction as a signal quality indication index. This was implemented by using machine learning (ML), with the best performances being achieved by support vector machine (SVM) models, based on the spectra acquired on the arm and forehead areas.

IntroductionRecordings of electroencephalographic (EEG) rhythms and their analyses have been instrumental in basic neuroscience, clinical diagnostics, and the field of brain-computer interfaces (BCIs). While in the past such measurements have been conducted mostly in laboratory settings, recent advancements in dry electrode technology pave way to a broader range of consumer and medical application because of their greater convenience compared to gel-based electrodes.MethodsHere we conducted resting-state EEG recordings in two groups of healthy participants using three dry-electrode devices, the PSBD Headband, the PSBD Headphones and the Muse Headband, and one standard gel electrode-based system, the NVX. We examined signal quality for various spatial and spectral ranges which are essential for cognitive monitoring and consumer applications.ResultsDistinctive characteristics of signal quality were found, with the PSBD Headband showing sensitivity in low-frequency ranges and replicating the modulations of delta, theta and alpha power corresponding to the eyes-open and eyes-closed conditions, and the NVX system performing well in capturing high-frequency oscillations. The PSBD Headphones were more prone to low-frequency artifacts compared to the PSBD Headband, yet recorded modulations in the alpha power and had a strong alignment with the NVX at the higher EEG frequencies. The Muse Headband had several limitations in signal quality.DiscussionWe suggest that while dry-electrode technology appears to be appropriate for the EEG rhythm-based applications, the potential benefits of these technologies in terms of ease of use and accessibility should be carefully weighed against the capacity of each given system.

In addition to the BeiDou regional navigation satellite system (BDS-2) backward compatible B1I and B3I signals, the BeiDou global navigation satellite system (BDS-3) also broadcast the B1C and B2a signals for compatibility with other Global Navigation Satellite System (GNSS), and providing satellite-based augmentation service for the International Civil Aviation Organization. Considering that the majority of the existing researches on the BDS-3 precise orbit determination (POD) and precise point positioning (PPP) are based on the B1I/B3I signals, the special attention that systematically comparing the data quality, POD accuracy and PPP performance through B1I/B3I and B1C/B2a signals is aroused in this paper. Signal characteristics of different receivers and antennas for the aforesaid four various signals were analyzed in terms of the carrier-to-noise density ratio (C/N0), pseudorange multipath and observation noise. The B1C signal exhibits the minimum C/N0 values, and the B3I and B2a signals have the smaller pseudorange multipath errors. The smallest pseudorange noise can be observed form the B2a signal, while the root mean squares (RMSs) for carrier phase noise show tiny differences among the four signals. The BDS-3 POD results indicate that the orbit overlapping arc error in three-dimensional (3D) direction is 0.069 and 0.076 m for the B1C/B2a and B1I/B3I, and the standard deviation of the satellite laser ranging validation is 0.041 m for both the two schemes. The clock offset products were evaluated with respect to the precise clock products from Deutsches GeoForschungsZentrum (GFZ), and the standard deviations (STDs) are 0.166 and 0.176 ns for the B1C/B2a and B1I/B3I. Finally, there is no apparent difference in the RMSs for B1C/B2a and B1I/B3I static PPP, while the convergence time that shortened by 31.4 % can be achieved for B1C/B2a. For kinematic PPP, the 3D RMS is 0.043 m and the convergence time is 31.1 min for the B1C/B2a, which is reduced by 14.0 % and 35.2 %, respectively, compared to B1I/B3I. [ABSTRACT FROM AUTHOR]

This work examines a number of beamforming techniques used for antennas operating at mmWave frequencies on high-frequency communications networks such as the fifth generation and beyond. Since mmWave has higher frequencies, it also provides far higher bandwidth and thus increased data rate and capacity. These advantages on the other hand are associated with disadvantages such as higher path loss and higher probability of obstruction. This work considers different forms of beamforming ; namely; analog, digital and hybrid for enhancing the quality of signals in systems using mmWave. Drawing out the pros and cons of the overall complexity, energy usage and efforts to maximize operational capabilities, we propose effective strategies for the various network conditions. Further, analysis of antenna placement, namely array configuration and element spacing, is provided in relation to actual beamforming effectiveness in practice, illustrating that various design factors cannot be overlooked. We use simulation and field trials to show that with recently proposed complex beamforming methods interferences can be handled and overall user experience is enhanced in highly dense urban settings. The studies show that it is possible to improve the performance of mmWave communication links, using adaptive beamforming techniques and machine learning, based on RSIs and CSI on the fly. The goal of this research is to contribute to the understanding of the imperatives of advanced beamforming techniques which are indispensable for the delivery of high speed, low latency new generation communication networks. [ABSTRACT FROM AUTHOR]

Aim: The aims of the study were to examine the signal quality (SQ) of home polygraphy (PG) in children and adolescents and to compare automatic and manual scoring of the PGs. Methods: Clinical Trials Registration: NCT04964830. Participants and caregivers were instructed to set up the equipment and perform home PGs themselves. The PGs were analysed according to SQ and their interpretability and differences in automatic vs. manual scoring regarding apnoea–hypopnoea index (AHI), apnoea index (AI), hypopnoea index (HI) and oxygen desaturation index (ODI) were examined. Results: 54 healthy children aged 9–14 years participated in the study. 86% of the PGs were interpretable with mean SQ of 79.1% (CI 95%: 73.5%; 84.8%). Significant differences between the automatic and manual scoring were found for AHI, AI, HI and ODI (p < 0.0001). Conclusion: Home PGs of children and adolescents are feasible to be performed with good SQ. Significantly higher markers of sleep‐disordered breathing were achieved in the automatic scoring in comparison with the manual scoring. [ABSTRACT FROM AUTHOR]

This paper provides novel insight into the growing literature on the policy uncertainty‐stock market volatility nexus by examining the out‐of‐sample predictive ability of the quality of political signals over stock market volatility at various forecast horizons. Specifically, we examine whether or not accounting for the signal quality in forecasting models within a mixed frequency framework can improve forecast performance and help achieve economic gains for investors. Both in‐ and out‐of‐sample tests, based on a GARCH‐MIDAS framework, show that the quality of the policy signal matters regarding the predictive role of policy uncertainty over subsequent stock market volatility. While high economic policy uncertainty (EPU) predicts high volatility, particularly when the signal quality is high, the positive relationship between EPU and volatility breaks down when the signal quality is low. The improved out‐of‐sample volatility forecasts obtained from the models that account for the quality of policy signals also help typical mean–variance investors achieve improved economic outcomes captured by higher certainty equivalent returns and Sharpe ratios. Although our results indicate clear distinctions between the US and UK stock markets in terms of how market participants process policy signals, they highlight the role of the quality of policy signals as a driver of volatility forecasts with significant economic implications. [ABSTRACT FROM AUTHOR]

Wearable in-ear electroencephalographic (EEG) devices hold significant promise for advancing brain monitoring technologies into everyday applications. However, despite the current availability of several in-ear EEG devices in the market, there remains a critical need for robust validation against established clinical-grade systems. In this study, we carried out a detailed examination of the signal performance of a mobile in-ear EEG device from Naox Technologies. Our investigation had two main goals: firstly, evaluating the hardware circuit’s reliability through simulated EEG signal experiments and, secondly, conducting a thorough comparison between the in-ear EEG device and gold-standard EEG monitoring equipment. This comparison assesses correlation coefficients with recognized physiological patterns during wakefulness and sleep, including alpha rhythms, eye artifacts, slow waves, spindles, and sleep stages. Our findings support the feasibility of using this in-ear EEG device for brain activity monitoring, particularly in scenarios requiring enhanced comfort and user-friendliness in various clinical and research settings.

Bioimpedance monitoring is an increasingly important non-invasive technique for assessing physiological parameters such as body composition, hydration levels, heart rate, and breathing. However, sensor signals obtained from real-world experimental conditions invariably contain noise, which can significantly degrade the reliability of the derived quantities. Therefore, it is crucial to evaluate the quality of measured signals to ensure accurate physiological parameter values. In this study, we present a novel wrist-worn wearable device for bioimpedance monitoring, and propose a method for estimating signal quality for sensor signals obtained on the device. The method is based on the continuous wavelet transform of the measured signal, identification of wavelet ridges, and assessment of their energy weighted by the ridge duration. We validate the algorithm using a small-scale experimental study with the wearable device, and explore the effects of variables such as window size and different skin/electrode coupling agents on signal quality and repeatability. In comparison with traditional wavelet-based signal denoising, the proposed method is more adaptive and achieves a comparable signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

Adaptive equalization is crucial in mitigating distortions and compensating for frequency response variations in communication systems. It aims to enhance signal quality by adjusting the characteristics of the received signal. Particle swarm optimization (PSO) algorithms have shown promise in optimizing the tap weights of the equalizer. However, there is a need to enhance the optimization capabilities of PSO further to improve the equalization performance. This paper provides a comprehensive study of the issues and challenges of adaptive filtering by comparing different variants of PSO and analyzing the performance by combining PSO with other optimization algorithms to achieve better convergence, accuracy, and adaptability. Traditional PSO algorithms often suffer from high computational complexity and slow convergence rates, limiting their effectiveness in solving complex optimization problems. To address these limitations, this paper proposes a set of techniques aimed at reducing the complexity and accelerating the convergence of PSO. [ABSTRACT FROM AUTHOR]

Noise and artifacts affect strongly the quality of the electrocardiogram (ECG) in long-term ECG monitoring (LTM), making some of its parts impractical for diagnosis. The clinical severity of noise defines a qualitative quality score according to the manner clinicians make the interpretation of the ECG, in contrast to assess noise from a quantitative standpoint. So clinical noise refers to a scale of different levels of qualitative severity of noise which aims at elucidating which ECG fragments are valid to achieve diagnosis from a clinical point of view, unlike the traditional approach, which assesses noise in terms of quantitative severity. This work proposes the use of machine learning (ML) techniques to categorize different qualitative noise severity using a database annotated according to a clinical noise taxonomy as gold standard. A comparative study is carried out using five representative ML methods, namely, K neareast neighbors, decision trees, support vector machine, single-layer perceptron, and random forest. The models are fed by signal quality indexes characterizing the waveform in time and frequency domains, as well as from a statistical viewpoint, to distinguish between clinically valid ECG segments from invalid ones. A solid methodology to prevent overfitting to both the dataset and the patient is developed, taking into account balance of classes, patient separation, and patient rotation in the test set. All the proposed learning systems have demonstrated good classification performance, attaining a recall, precision, and F1 score up to 0.78, 0.80, and 0.77, respectively, in the test set by a single-layer perceptron approach. These systems provide a classification solution for assessing the clinical quality of the ECG taken from LTM recordings. [ABSTRACT FROM AUTHOR]

Steady-state visual-evoked potentials (SSVEPs) are widely used in human neuroscience studies and applications such as braincomputer interfaces (BCIs). Surprisingly, no previous study has systematically evaluated different reference methods for SSVEP analysis, despite that signal reference is crucial for the proper assessment of neural activities. In the present study, using four datasets from our previous SSVEP studies (Chen J, Valsecchi M, Gegenfurtner KR. J Neurophysiol 118: 749–754, 2017; Chen J, Valsecchi M, Gegenfurtner KR. Neuropsychologia 102: 206–216, 2017; Chen J, McManus M, Valsecchi M, Harris LR, Gegenfurtner KR. J Vis 19: 8, 2019) and three public datasets from other studies (Baker DH, Vilidaite G, Wade AR. PLoS Comput Biol 17: e1009507, 2021; Lygo FA, Richard B, Wade AR, Morland AB, Baker DH. NeuroImage 230: 117780, 2021; Vilidaite G, Norcia AM, West RJH, Elliott CJH, Pei F, Wade AR, Baker DH. Proc R Soc B 285: 20182255, 2018), we compared four reference methods: monopolar reference, common average reference, averaged-mastoids reference, and Laplacian reference. The quality of the resulting SSVEP signals was compared in terms of both signal-to-noise ratios (SNRs) and reliability. The results showed that Laplacian reference, which uses signals at the maximally activated electrode after subtracting the average of the nearby electrodes to reduce common noise, gave rise to the highest SNRs. Furthermore, the Laplacian reference resulted in SSVEP signals that were highly reliable across recording sessions or trials. These results suggest that Laplacian reference is optimal for SSVEP studies and applications. Laplacian reference is especially advantageous for SSVEP experiments where short preparation time is preferred as it requires only data from the maximally activated electrode and a few surrounding electrodes. NEW & NOTEWORTHY The present study provides a comprehensive evaluation of the use of different reference methods for steady-state visual-evoked potentials (SSVEPs) and has found that Laplacian reference increases signal-to-noise ratios (SNRs) and enhances reliabilities of SSVEP signals. Thus, the results suggest that Laplacian reference is optimal for SSVEP analysis. [ABSTRACT FROM AUTHOR]

Since electroencephalogram (EEG) is a very small electrical signal from the brain, it is very vulnerable to external noise or motion artifact, making it difficult to measure. Therefore, despite the excellent convenience of dry electrodes, wet electrodes have been used. To solve this problem, self-adhesive and conductive composites using carbon nanotubes (CNTs) in adhesive polydimethylsiloxane (aPDMS), which can have the advantages of both dry and wet electrodes, have been developed by mixing them uniformly with methyl group-terminated PDMS. The CNT/aPDMS composite has a low Young's modulus, penetrates the skin well, has a high contact area, and excellent adhesion and conductivity, so the signal quality is enhanced. As a result of the EEG measurement test, although it was a dry electrode, results comparable to those of a wet electrode were obtained in terms of impedance and motion noise. It also shows excellent biocompatibility in a human fibroblast cell test and a week-long skin reaction test, so it can measure EEG with high signal quality for a long period of time. [ABSTRACT FROM AUTHOR]

Early detection of Atrial Fibrillation (AFib) is crucial to prevent stroke recurrence. New tools for monitoring cardiac rhythm are important for risk stratification and stroke prevention. As many of new approaches to long-term AFib detection are now based on photoplethysmogram (PPG) recordings from wearable devices, ensuring high PPG signal-to-noise ratios is a fundamental requirement for a robust detection of AFib episodes. Traditionally, signal quality assessment is often based on the evaluation of similarity between pulses to derive signal quality indices. There are limitations to using this approach for accurate assessment of PPG quality in the presence of arrhythmia, as in the case of AFib, mainly due to substantial changes in pulse morphology. In this paper, we first tested the performance of algorithms selected from a body of studies on PPG quality assessment using a dataset of PPG recordings from patients with AFib. We then propose machine learning approaches for PPG quality assessment in 30-s segments of PPG recording from 13 stroke patients admitted to the University of California San Francisco (UCSF) neuro intensive care unit and another dataset of 3764 patients from one of the five UCSF general intensive care units. We used data acquired from two systems, fingertip PPG (fPPG) from a bedside monitor system, and radial PPG (rPPG) measured using a wearable commercial wristband. We compared various supervised machine learning techniques including k-nearest neighbors, decisions trees, and a two-class support vector machine (SVM). SVM provided the best performance. fPPG signals were used to build the model and achieved 0.9477 accuracy when tested on the data from the fPPG exclusive to the test set, and 0.9589 accuracy when tested on the rPPG data.

Pulse code modulation (PCM) is an essential technique in digital communications, which transforms analog signals into digital data for transmission and processing. This technique is based on three fundamental processes: sampling, quantization and coding. With the growing demands on communication efficiency and quality, the optimization of PCM systems becomes crucial. This article explores advanced methods for the design and optimization of PCM systems, highlighting the use of sophisticated tools and algorithms to improve the overall performance of these systems. Approaches for bandwidth reduction, signal quality improvement, and error minimization are addressed. Innovative techniques such as variable pulse length coding and the use of optimization algorithms, such as genetic algorithms and the particle swarm optimization algorithm, are presented. Through practical case studies, it is demonstrated how these techniques can be applied to obtain superior results on real systems. This paper provides a comprehensive overview of advances in PCM modulation and its application in digital communication enhancement, offering recommendations for future research and applications in high-demand environments. [ABSTRACT FROM AUTHOR]

This paper presents the performance analysis of CentiSpace low earth orbit (LEO) experiment satellites. Distinguishing them from other LEO navigation augmentation systems, the co-time and co-frequency (CCST) self-interference suppression technique is employed in CentiSpace to mitigate significant self-interference caused by augmentation signals. Consequently, CentiSpace exhibits the capability of receiving navigation signals from the Global Navigation Satellite System (GNSS) while simultaneously broadcasting augmentation signals within the same frequency bands, thus ensuring excellent compatibility for GNSS receivers. CentiSpace is a pioneering LEO navigation system to successfully complete in-orbit verification of this technique. Leveraging the on-board experiment data, this study analyzes the performance of space-borne GNSS receivers equipped with self-interference suppression and evaluates the quality of navigation augmentation signals. The results show that CentiSpace space-borne GNSS receivers are capable of covering more than 90% visible GNSS satellites and the precision of self-orbit determination is at the centimeter level. Furthermore, the quality of augmentation signals meets the requirements outlined in the BDS interface control documents. These findings underscore the potential of the CentiSpace LEO augmentation system for the establishment of global integrity monitoring and GNSS signal augmentation. Moreover, these results contribute to subsequent research on LEO augmentation techniques. [ABSTRACT FROM AUTHOR]

A first-level textile-based electrocardiogram (ECG) monitoring system referred to as "CardioS" (cardiac sensor) for continuous health monitoring applications is proposed in this study to address the demand for resource-constrained environments. and the signal quality assessment of a wireless CardioS was studied. The CardioS consists of a Lead-I ECG signal recorded wirelessly using silver-plated nylon woven (Ag-NyW) dry textile electrodes to compare the results of wired wearable Ag-NyW textile electrode-based ECG acquisition system and CardioS. The effect of prolonged usage of Ag-NyW dry electrodes on electrode impedance was tested in the current work. In addition, electrode half-cell potential was measured to validate the range of Ag-NyW dry electrodes for ECG signal acquisition. Further, the quality of signals recorded by the proposed wireless CardioS framework was evaluated and compared with clinical disposable (Ag–AgCl Gel) electrodes. The signal quality was assessed in terms of mean magnitude coherence spectra, signal cross-correlation, signal-to-noise-band ratio (Sband/Nband), crest factor, low and high band powers and power spectral density. The experimental results showed that the impedance was increased by 2.5–54.6% after six weeks of continuous usage. This increased impedance was less than 1 MΩ/cm2, as reported in the literature. The half-cell potential of the Ag-NyW textile electrode obtained was 80 mV, sufficient to acquire the ECG signal from the human body. All the fidelity parameters measured by Ag-NyW textile electrodes were correlated with standard disposable electrodes. The cardiologists validated all the measurements and confirmed that the proposed framework exhibited good performance for ECG signal acquisition from the five healthy subjects. As a result of its low-cost architecture, the proposed CardioS framework can be used in resource-constrained environments for ECG monitoring. [ABSTRACT FROM AUTHOR]

Introduction: Non‐invasive fetal electrocardiography (NIFECG) has potential benefits over the computerized cardiotocography (cCTG) that may permit its development in remote fetal heart‐rate monitoring. Our study aims to compare signal quality and heart‐rate detection from a novel self‐applicable NIFECG monitor against the cCTG, and evaluate the impact of maternal and fetal characteristics on both devices. Material and methods: This prospective observational study took place in a university hospital in London. Women with a singleton pregnancy from 28 + 0 weeks' gestation presenting for cCTG were eligible. Concurrent monitoring with both NIFECG and cCTG were performed for up to 60 minutes. Post‐processing of NIFECG produced signal loss, computed in both 0.25 (E240)‐ and 3.75 (E16)‐second epochs, and fetal heart‐rate and maternal heart‐rate values. cCTG signal loss was calculated in 3.75‐second epochs. Accuracy and precision analysis of 0.25‐second epochal fetal heart‐rate and maternal heart‐rate were compared between the two devices. Multiple regression analyses were performed to assess the impact of maternal and fetal characteristics on signal loss. ClinicalTrials.gov Identifier: NCT04941534. Results: 285 women underwent concurrent monitoring. For fetal heart‐rate, mean bias, precision and 95% limits of agreement were 0.1 beats per minute (bpm), 4.5 bpm and −8.7 bpm to 8.8 bpm, respectively. For maternal heart‐rate, these results were −0.4 bpm, 3.3 bpm and −7.0 to 6.2 bpm, respectively. Median NIFECG E240 and E16 signal loss was 32.0% (interquartile range [IQR] 6.5%–68.5%) and 17.3% (IQR 1.8%–49.0%), respectively. E16 cCTG signal loss was 1.0% (IQR 0.0%–3.0%). For NIFECG, gestational age was negatively associated with signal loss (beta = −2.91, 95% CI −3.69 to −2.12, P < 0.001). Increased body mass index, fetal movements and lower gestational age were all associated with cCTG signal loss (beta = 0.30, 95% CI 0.17–0.43, P < 0.001; beta = 0.03, 95% CI 0.01–0.05, P = 0.014; and beta = −0.28, 95% CI −0.51 to −0.05, P = 0.017, respectively). Conclusions: Although NIFECG is complicated by higher signal loss, it does not appear to be influenced by increased body mass index or fetal movement. NIFECG signal loss varies according to method of computation, and standards of signal acceptability need to be defined according to the ability of the device to produce clinically reliable physiological indices. The high accuracy of heart‐rate indices is promising for NIFECG usage in the remote setting. [ABSTRACT FROM AUTHOR]

Epilepsy is a neurological disorder that causes repeated seizures in millions of people worldwide. Traditional Electroencephalography (EEG) systems can be cumbersome and limited to clinical settings, but they have helped diagnose and monitor epilepsy. Wearable EEG devices have transformed epilepsy management by providing real-time, non-invasive, and continuous monitoring capabilities. This review paper investigates the design considerations and technological advancements in wearable EEG devices, emphasizing their numerous benefits in treating epileptic patients and the limitation of designing wearable devices. In conclusion, the integration of multimodal data can offer a comprehensive overview of a patient's health, enabling the implementation of personalized and efficient treatment approaches.

Ambulatory EEGs began emerging in the healthcare industry over the years, setting a new norm for long-term monitoring services. The present devices in the market are neither meant for remote monitoring due to their technical complexity nor for meeting clinical setting needs in epilepsy patient monitoring. In this paper, we propose an ambulatory EEG device, OptiEEG, that has low setup complexity, for the remote EEG monitoring of epilepsy patients. OptiEEG's signal quality was compared with a gold standard clinical device, Natus. The experiment between OptiEEG and Natus included three different tests: eye open/close (EOC); hyperventilation (HV); and photic stimulation (PS). Statistical and wavelet analysis of retrieved data were presented when evaluating the performance of OptiEEG. The SNR and PSNR of OptiEEG were slightly lower than Natus, but within an acceptable bound. The standard deviations of MSE for both devices were almost in a similar range for the three tests. The frequency band energy analysis is consistent between the two devices. A rhythmic slowdown of theta and delta was observed in HV, whereas photic driving was observed during PS in both devices. The results validated the performance of OptiEEG as an acceptable EEG device for remote monitoring away from clinical environments. [ABSTRACT FROM AUTHOR]

Photoplethysmography (PPG) signal quality as a proxy for accuracy in heart rate (HR) measurement is useful in various public health contexts, ranging from short-term clinical diagnostics to free-living health behavior surveillance studies that inform public health policy. Each context has a different tolerance for acceptable signal quality, and it is reductive to expect a single threshold to meet the needs across all contexts. In this study, we propose two different metrics as sliding scales of PPG signal quality and assess their association with accuracy of HR measures compared to a ground truth electrocardiogram (ECG) measurement. Methods: We used two publicly available PPG datasets (BUT PPG and Troika) to test if our signal quality metrics could identify poor signal quality compared to gold standard visual inspection. To aid interpretation of the sliding scale metrics, we used ROC curves and Kappa values to calculate guideline cut points and evaluate agreement, respectively. We then used the Troika dataset and an original dataset of PPG data collected from the chest to examine the association between continuous metrics of signal quality and HR accuracy. PPG-based HR estimates were compared with reference HR estimates using the mean absolute error (MAE) and the root-mean-square error (RMSE). Point biserial correlations were used to examine the association between binary signal quality and HR error metrics (MAE and RMSE). Results: ROC analysis from the BUT PPG data revealed that the AUC was 0.758 (95% CI 0.624 to 0.892) for signal quality metrics of STD-width and 0.741 (95% CI 0.589 to 0.883) for self-consistency. There was a significant correlation between criterion poor signal quality and signal quality metrics in both Troika and originally collected data. Signal quality was highly correlated with HR accuracy (MAE and RMSE, respectively) between PPG and ground truth ECG. Conclusion: This proof-of-concept work demonstrates an effective approach for assessing signal quality and demonstrates the effect of poor signal quality on HR measurement. Our continuous signal quality metrics allow estimations of uncertainties in other emergent metrics, such as energy expenditure that relies on multiple independent biometrics. This open-source approach increases the availability and applicability of our work in public health settings. [ABSTRACT FROM AUTHOR]

Continuously acquired biosignals from patient monitors contain significant amounts of unusable data. During the development of a decision support system based on continuously acquired biosignals, we developed machine and deep learning algorithms to automatically classify the quality of ECG data. A total of 31,127 twenty-s ECG segments of 250 Hz were used as the training/validation dataset. Data quality was categorized into three classes: acceptable, unacceptable, and uncertain. In the training/validation dataset, 29,606 segments (95%) were in the acceptable class. Two one-step, three-class approaches and two two-step binary sequential approaches were developed using random forest (RF) and two-dimensional convolutional neural network (2D CNN) classifiers. Four approaches were tested on 9779 test samples from another hospital. On the test dataset, the two-step 2D CNN approach showed the best overall accuracy (0.85), and the one-step, three-class 2D CNN approach showed the worst overall accuracy (0.54). The most important parameter, precision in the acceptable class, was greater than 0.9 for all approaches, but recall in the acceptable class was better for the two-step approaches: one-step (0.77) vs. two-step RF (0.89) and one-step (0.51) vs. two-step 2D CNN (0.94) (p < 0.001 for both comparisons). For the ECG quality classification, where substantial data imbalance exists, the 2-step approaches showed more robust performance than the one-step approach. This algorithm can be used as a preprocessing step in artificial intelligence research using continuously acquired biosignals. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVD) represent a serious health problem worldwide, of which atrial fibrillation (AF) is one of the most common conditions. Early and timely diagnosis of CVD is essential for successful treatment. When implemented in the healthcare system this can ease the existing socio-economic burden on health institutions and government. Therefore, developing technologies and tools to diagnose CVD in a timely way and detect AF is an important research topic. ECG monitoring patches allowing ambulatory patient monitoring over several days represent a novel technology, while we witness a significant proliferation of ECG monitoring patches on the market and in the research labs, their performance over a long period of time is not fully characterized. This paper analyzes the signal quality of ECG signals obtained using a single-lead ECG patch featuring self-adhesive dry electrode technology collected from six cardiac patients for 5 days. In particular, we provide insights into signal quality degradation over time, while changes in the average ECG quality per day were present, these changes were not statistically significant. It was observed that the quality was higher during the nights, confirming the link with motion artifacts. These results can improve CVD diagnosis and AF detection in real-world scenarios. [ABSTRACT FROM AUTHOR]