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Introduction: Though wrist-worn photoplethysmography (PPG) sensors play an important role in long-term and continuous heart rhythm monitoring, signals measured at the wrist are contaminated by more intense motion artifacts compared to other body locations. Machine learning (ML)-based algorithms can improve long-term pulse rate (PR) tracking but are associated with more stringent regulatory requirements when intended for clinical use. This study aimed to evaluate the accuracy of a digital health technology using wrist-worn PPG sensors and an ML-based algorithm to measure PR continuously., Methods: Volunteers were enrolled in three independent clinical trials and concurrently monitored with the investigational device and FDA-cleared electrocardiography (ECG) devices during supervised protocols representative of real-life activities. The primary acceptance threshold was an accuracy root-mean-square (ARMS) ≤3 beats per minute (bpm) or 5 bpm under no-motion and motion conditions, respectively. Bias, mean absolute error (MAE), mean absolute percentage error (MAPE), limits of agreement (LoA), and Pearson and Lin's concordance correlation coefficients (⍴ and CCC) were also computed. Subgroup and outlier analyses were conducted to examine the effect of site, skin tone, age, sex, body mass index (BMI), and health status on PR accuracy., Results: Collectively, 16,915 paired observations between the device and the reference ECG were analyzed from 157 subjects (male: 49.04%, age mean: 43 years, age range: 19-83 years, BMI mean: 26.4, BMI range: 17.5-52, Fitzpatrick class V-IV: 22.9%, cardiovascular condition: 24%). The PR output attained an accuracy of 1.67 bpm under no-motion ( n = 5,621 min) and 4.39 bpm under motion ( n = 11,294 min), satisfying the acceptance thresholds. Bias and LoA (lower, upper LoA) were -0.09 (-3.36, 3.17) bpm under no-motion and 0.51 (-8.05, 9.06) bpm under motion. MAE was 0.6 bpm in no-motion and 1.77 bpm in motion, and MAPE was 0.86% in no-motion and 2.05% in motion, with ⍴ and CCC >0.98 in both conditions. ARMS values met the clinical acceptance threshold in all relevant subgroups at each clinical site separately, excluding male subjects under motion conditions (ARMS = 5.41 bpm), with more frequent and larger outliers due to stronger forearm contractions. However, these mostly occurred in isolation and, therefore would not impact the clinical utility or usability of the device for its intended use of retrospective review and trend analysis (⍴ and CCC >0.97 and MAPE = 2.61%)., Conclusion: The analytical validation conducted in this study demonstrated clinical-grade accuracy and generalizability of ML-based continuous PR estimations across a full range of physical motions, health conditions, and demographic variables known to confound PPG signals, paving the way for device usage by populations most likely to benefit from continuous PR monitoring., Competing Interests: All authors disclose being share-holders of Empatica Inc. and having received a salary or consulting fees from Empatica Inc., which manufactured the EmbracePlus wristband validated in this work. Moreover, Rosalind Picard is also a co-founder and member of the board of Empatica Inc., a shareholder in Smart Eye AB, and has received consulting fees from Apple, Samsung, Harman, and Alphainsights, and speaker fees from Stern Strategy, Amazon, DE Shaw, KBTG, and Amicus Rx., (© 2024 The Author(s). Published by S. Karger AG, Basel.)

Objective: This study aimed to assess whether population-level patterns in seizure occurrence previously observed in self-reported diaries, medical records, and electroencephalographic recordings were also present in tonic-clonic seizure (TCS) diaries produced via the combined input of a US Food and Drug Administration-cleared wristband with an artificial intelligence detection algorithm and patient self-reports. We also investigated the characteristics of patient interactions with wearable seizure alerts., Methods: We analyzed wristband data from patients with TCSs who had at least three reported TCSs over a minimum of 90 days. We quantified TCS frequency and cycles, and the relationship between the mean and variability of monthly TCS counts. We also assessed interaction metrics such as false alarm dismissal and seizure confirmation rates., Results: Applying strict criteria for usable data, we reviewed 137 490 TCSs from 3012 patients, with a median length of TCS alert records of 445 days (range = 90-1806). Analyses showed consistency between prior diary studies and the present data concerning (1) the distribution of monthly TCS frequency (median = 3.1, range = .08-26); (2) the linear relationship (slope = .79, R 2  = .83) between the logarithm of the mean and the logarithm of the SD of monthly TCS frequency (L-relationship); and (iii) the prevalence of multiple coexisting seizure cycles, including circadian (84.0%), weekly (24.6%), and long-term cycles (31.1%)., Significance: Key population-level patterns in seizure occurrence are recapitulated in wrist-worn device recordings, supporting their validity for tracking TCS burden. Compared to other approaches, wearables can provide noninvasive, objective, long-term data, revealing cycles in seizure risk. However, improved patient engagement with wristband alerts and further validation of detection accuracy in ambulatory settings are needed. Together, these findings suggest that data from smart wristbands may be used to derive features of TCS records and, ultimately, facilitate remote monitoring and the development of personalized forecasting tools for TCS management. Our findings may not generalize to other types of seizures., (© 2024 The Author(s). Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

Introduction: Respiratory diseases such as chronic obstructive pulmonary disease, obstructive sleep apnea syndrome, and COVID-19 may cause a decrease in arterial oxygen saturation (SaO 2 ). The continuous monitoring of oxygen levels may be beneficial for the early detection of hypoxemia and timely intervention. Wearable non-invasive pulse oximetry devices measuring peripheral oxygen saturation (SpO 2 ) have been garnering increasing popularity. However, there is still a strong need for extended and robust clinical validation of such devices, especially to address topical concerns about disparities in performances across racial groups. This prospective clinical validation aimed to assess the accuracy of the reflective pulse oximeter function of the EmbracePlus wristband during a controlled hypoxia study in accordance with the ISO 80601-2-61:2017 standard and the Food & Drug Administration (FDA) guidance., Methods: Healthy adult participants were recruited in a controlled desaturation protocol to reproduce mild, moderate, and severe hypoxic conditions with SaO 2 ranging from 100% to 70% (ClinicalTrials.gov registration #NCT04964609). The SpO 2 level was estimated with an EmbracePlus device placed on the participant's wrist and the reference SaO 2 was obtained from blood samples analyzed with a multiwavelength co-oximeter., Results: The controlled hypoxia study yielded 373 conclusive measurements on 15 subjects, including 30% of participants with dark skin pigmentation (V-VI on the Fitzpatrick scale). The accuracy root mean square ( A rms ) error was found to be 2.4%, within the 3.5% limit recommended by the FDA. A strong positive correlation between the wristband SpO 2 and the reference SaO 2 was observed ( r  = 0.96, P  < 0.001), and a good concordance was found with Bland-Altman analysis (bias, 0.05%; standard deviation, 1.66; lower limit, -4.7%; and upper limit, 4.8%). Moreover, acceptable accuracy was observed when stratifying data points by skin pigmentation ( A rms 2.2% in Fitzpatrick V-VI, 2.5% in Fitzpatrick I-IV), and sex ( A rms 1.9% in females, and 2.9% in males)., Discussion: This study demonstrates that the EmbracePlus wristband could be used to assess SpO 2 with clinically acceptable accuracy under no-motion and high perfusion conditions for individuals of different ethnicities across the claimed range. This study paves the way for further accuracy evaluations on unhealthy subjects and during prolonged use in ambulatory settings., Competing Interests: All authors disclose being share-holders of Empatica Inc. and having received a salary or consulting fees from Empatica Inc., which manufactured the EmbracePlus wristband validated in this work. Moreover, RP is a co-founder and chairman of the board of Empatica Inc., (© 2023 Gerboni, Comunale, Chen, Lever Taylor, Migliorini, Picard, Cruz and Regalia.)

Background: Using machine learning to combine wrist accelerometer (ACM) and electrodermal activity (EDA) has been shown effective to detect primarily and secondarily generalized tonic-clonic seizures, here termed as convulsive seizures (CS). A prospective study was conducted for the FDA clearance of an ACM and EDA-based CS-detection device based on a predefined machine learning algorithm. Here we present its performance on pediatric and adult patients in epilepsy monitoring units (EMUs). Methods: Patients diagnosed with epilepsy participated in a prospective multi-center clinical study. Three board-certified neurologists independently labeled CS from video-EEG. The Detection Algorithm was evaluated in terms of Sensitivity and false alarm rate per 24 h-worn (FAR) on all the data and on only periods of rest. Performance were analyzed also applying the Detection Algorithm offline, with a less sensitive but more specific parameters configuration ("Active mode"). Results: Data from 152 patients (429 days) were used for performance evaluation (85 pediatric aged 6-20 years, and 67 adult aged 21-63 years). Thirty-six patients (18 pediatric) experienced a total of 66 CS (35 pediatric). The Sensitivity (corrected for clustered data) was 0.92, with a 95% confidence interval (CI) of [0.85-1.00] for the pediatric population, not significantly different ( p > 0.05) from the adult population's Sensitivity (0.94, CI: [0.89-1.00]). The FAR on the pediatric population was 1.26 (CI: [0.87-1.73]), higher ( p < 0.001) than in the adult population (0.57, CI: [0.36-0.81]). Using the Active mode, the FAR decreased by 68% while reducing Sensitivity to 0.95 across the population. During rest periods, the FAR's were 0 for all patients, lower than during activity periods ( p < 0.001). Conclusions: Performance complies with FDA's requirements of a lower bound of CI for Sensitivity higher than 0.7 and of a FAR lower than 2, for both age groups. The pediatric FAR was higher than the adult FAR, likely due to higher pediatric activity. The high Sensitivity and precision (having no false alarms) during sleep might help mitigate SUDEP risk by summoning caregiver intervention. The Active mode may be advantageous for some patients, reducing the impact of the FAR on daily life. Future work will examine the performance and usability outside of EMUs., Competing Interests: FO, GR, and CC are shareholders of Empatica Inc., which manufactured two of the devices used in this work and developed the two new algorithms tested in this work. GR is also an employee of Empatica and RP is also a consultant and chairs the board of directors for Empatica. TL is part of pending patent applications to detect and predict seizures and to diagnose epilepsy with devices different from the ones used in this work and has received research support from Empatica to conduct this research. TL, WL, and AB have received sensors from Empatica to perform the reported research. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Onorati, Regalia, Caborni, LaFrance, Blum, Bidwell, De Liso, El Atrache, Loddenkemper, Mohammadpour-Touserkani, Sarkis, Friedman, Jeschke and Picard.)

The purpose of the present work is to examine, on a clinically diverse population of older adults (N = 46) sleeping at home, the performance of two actigraphy-based sleep tracking algorithms (i.e., Actigraphy-based Sleep algorithm, ACT-S1 and Sadeh's algorithm) compared to manually scored electroencephalography-based PSG (PSG-EEG). ACT-S1 allows for a fully automatic identification of sleep period time (SPT) and within the identified sleep period, the sleep-wake classification. SPT detected by ACT-S1 did not differ statistically from using PSG-EEG (bias = -9.98 min; correlation 0.89). In sleep-wake classification on 30-s epochs within the identified sleep period, the new ACT-S1 presented similar or slightly higher accuracy (83-87%), precision (86-89%) and F1 score (90-92%), significantly higher specificity (39-40%), and significantly lower, but still high, sensitivity (96-97%) compared to Sadeh's algorithm, which achieved 99% sensitivity as the only measure better than ACT-S1's. Total sleep times (TST) estimated with ACT-S1 and Sadeh's algorithm were higher, but still highly correlated to PSG-EEG's TST. Sleep quality metrics of sleep period efficiency and wake-after-sleep-onset computed by ACT-S1 were not significantly different from PSG-EEG, while the same sleep quality metrics derived by Sadeh's algorithm differed significantly from PSG-EEG. Agreement between ACT-S1 and PSG-EEG reached was highest when analyzing the subset of subjects with least disrupted sleep (N = 28). These results provide evidence of promising performance of a full-automation of the sleep tracking procedure with ACT-S1 on older adults. Future longitudinal validations across specific medical conditions are needed. The algorithm's performance may further improve with integrating multi-sensor information.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease affecting the corticospinal tract and leading to motor neuron death. According to a recent study, magnetic resonance imaging-visible changes suggestive of neurodegeneration seem absent in the motor cortex of G93A-SOD1 ALS mice. However, it has not yet been ascertained whether the cortical neural activity is intact, or alterations are present, perhaps even from an early stage. Here, cortical neurons from this model were isolated at post-natal day 1 and cultured on multielectrode arrays. Their activity was studied with a comprehensive pool of neurophysiological analyses probing excitability, criticality and network architecture, alongside immunocytochemistry and molecular investigations. Significant hyperexcitability was visible through increased network firing rate and bursting, whereas topological changes in the synchronization patterns were apparently absent. The number of dendritic spines was increased, accompanied by elevated transcriptional levels of the DLG4 gene, NMDA receptor 1 and the early pro-apoptotic APAF1 gene. The extracellular Na + , Ca 2+ , K + and Cl - concentrations were elevated, pointing to perturbations in the culture micro-environment. Our findings highlight remarkable early changes in ALS cortical neuron activity and physiology. These changes suggest that the causative factors of hyperexcitability and associated toxicity could become established much earlier than the appearance of disease symptoms, with implications for the discovery of new hypothetical therapeutic targets., (Copyright © 2019 IBRO. Published by Elsevier Ltd. All rights reserved.)

Wearable automated seizure detection devices offer a high potential to improve seizure management, through continuous ambulatory monitoring, accurate seizure counts, and real-time alerts for prompt intervention. More importantly, these devices can be a life-saving help for people with a higher risk of sudden unexpected death in epilepsy (SUDEP), especially in case of generalized tonic-clonic seizures (GTCS). The Embrace and E4 wristbands (Empatica) are the first commercially available multimodal wristbands that were designed to sense the physiological hallmarks of ongoing GTCS: while Embrace only embeds a machine learning-based detection algorithm, both E4 and Embrace devices are equipped with motion (accelerometers, ACC) and electrodermal activity (EDA) sensors and both the devices received medical clearance (E4 from EU CE, Embrace from EU CE and US FDA). The aim of this contribution is to provide updated evidence of the effectiveness of GTCS detection and monitoring relying on the combination of ACM and EDA sensors. A machine learning algorithm able to recognize ACC and EDA signatures of GTCS-like events has been developed on E4 data, labeled using gold-standard video-EEG examined by epileptologists in clinical centers, and has undergone continuous improvement. While keeping an elevated sensitivity to GTCS (92-100%), algorithm improvements and growing data availability led to lower false alarm rate (FAR) from the initial ˜2 down to 0.2-1 false alarms per day, as showed by retrospective and prospective analyses in inpatient settings. Algorithm adjustment to better discriminate real-life physical activities from GTCS, has brought the initial FAR of ˜6 on outpatient real life settings, down to values comparable to best-case clinical settings (FAR < 0.5), with comparable sensitivity. Moreover, using multimodal sensing, it has been possible not only to detect GTCS but also to quantify seizure-induced autonomic dysfunction, based on automatic features of abnormal motion and EDA. The latter biosignal correlates with the duration of post-ictal generalized EEG suppression, a biomarker observed in 100% of monitored SUDEP cases., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Objective: New devices are needed for monitoring seizures, especially those associated with sudden unexpected death in epilepsy (SUDEP). They must be unobtrusive and automated, and provide false alarm rates (FARs) bearable in everyday life. This study quantifies the performance of new multimodal wrist-worn convulsive seizure detectors., Methods: Hand-annotated video-electroencephalographic seizure events were collected from 69 patients at six clinical sites. Three different wristbands were used to record electrodermal activity (EDA) and accelerometer (ACM) signals, obtaining 5,928 h of data, including 55 convulsive epileptic seizures (six focal tonic-clonic seizures and 49 focal to bilateral tonic-clonic seizures) from 22 patients. Recordings were analyzed offline to train and test two new machine learning classifiers and a published classifier based on EDA and ACM. Moreover, wristband data were analyzed to estimate seizure-motion duration and autonomic responses., Results: The two novel classifiers consistently outperformed the previous detector. The most efficient (Classifier III) yielded sensitivity of 94.55%, and an FAR of 0.2 events/day. No nocturnal seizures were missed. Most patients had <1 false alarm every 4 days, with an FAR below their seizure frequency. When increasing the sensitivity to 100% (no missed seizures), the FAR is up to 13 times lower than with the previous detector. Furthermore, all detections occurred before the seizure ended, providing reasonable latency (median = 29.3 s, range = 14.8-151 s). Automatically estimated seizure durations were correlated with true durations, enabling reliable annotations. Finally, EDA measurements confirmed the presence of postictal autonomic dysfunction, exhibiting a significant rise in 73% of the convulsive seizures., Significance: The proposed multimodal wrist-worn convulsive seizure detectors provide seizure counts that are more accurate than previous automated detectors and typical patient self-reports, while maintaining a tolerable FAR for ambulatory monitoring. Furthermore, the multimodal system provides an objective description of motor behavior and autonomic dysfunction, aimed at enriching seizure characterization, with potential utility for SUDEP warning., (Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.)

The CRISPR/Cas9 system is a rapid and customizable tool for gene editing in mammalian cells. In particular, this approach has widely opened new opportunities for genetic studies in neurological disease. Human neurons can be differentiated in vitro from hPSC (human Pluripotent Stem Cells), hNPCs (human Neural Precursor Cells) or even directly reprogrammed from fibroblasts. Here, we described a new platform which enables, rapid and efficient CRISPR/Cas9-mediated genome targeting simultaneously with three different paradigms for in vitro generation of neurons. This system was employed to inactivate two genes associated with neurological disorder (TSC2 and KCNQ2) and achieved up to 85% efficiency of gene targeting in the differentiated cells. In particular, we devised a protocol that, combining the expression of the CRISPR components with neurogenic factors, generated functional human neurons highly enriched for the desired genome modification in only 5 weeks. This new approach is easy, fast and that does not require the generation of stable isogenic clones, practice that is time consuming and for some genes not feasible.

Two binding requirements for in vitro studies on long-term neuronal networks dynamics are (i) finely controlled environmental conditions to keep neuronal cultures viable and provide reliable data for more than a few hours and (ii) parallel operation on multiple neuronal cultures to shorten experimental time scales and enhance data reproducibility. In order to fulfill these needs with a Microelectrode Arrays (MEA)-based system, we designed a stand-alone device that permits to uninterruptedly monitor neuronal cultures activity over long periods, overcoming drawbacks of existing MEA platforms. We integrated in a single device: (i) a closed chamber housing four MEAs equipped with access for chemical manipulations, (ii) environmental control systems and embedded sensors to reproduce and remotely monitor the standard in vitro culture environment on the lab bench (i.e. in terms of temperature, air CO2 and relative humidity), and (iii) a modular MEA interface analog front-end for reliable and parallel recordings. The system has been proven to assure environmental conditions stable, physiological and homogeneos across different cultures. Prolonged recordings (up to 10 days) of spontaneous and pharmacologically stimulated neuronal culture activity have not shown signs of rundown thanks to the environmental stability and have not required to withdraw the cells from the chamber for culture medium manipulations. This system represents an effective MEA-based solution to elucidate neuronal network phenomena with slow dynamics, such as long-term plasticity, effects of chronic pharmacological stimulations or late-onset pathological mechanisms., (© 2015 Wiley Periodicals, Inc.)

Neuronal spike sorting algorithms are designed to retrieve neuronal network activity on a single-cell level from extracellular multiunit recordings with Microelectrode Arrays (MEAs). In typical analysis of MEA data, one spike sorting algorithm is applied indiscriminately to all electrode signals. However, this approach neglects the dependency of algorithms' performances on the neuronal signals properties at each channel, which require data-centric methods. Moreover, sorting is commonly performed off-line, which is time and memory consuming and prevents researchers from having an immediate glance at ongoing experiments. The aim of this work is to provide a versatile framework to support the evaluation and comparison of different spike classification algorithms suitable for both off-line and on-line analysis. We incorporated different spike sorting "building blocks" into a Matlab-based software, including 4 feature extraction methods, 3 feature clustering methods, and 1 template matching classifier. The framework was validated by applying different algorithms on simulated and real signals from neuronal cultures coupled to MEAs. Moreover, the system has been proven effective in running on-line analysis on a standard desktop computer, after the selection of the most suitable sorting methods. This work provides a useful and versatile instrument for a supported comparison of different options for spike sorting towards more accurate off-line and on-line MEA data analysis.

The collection of good quality extracellular neuronal spikes from neuronal cultures coupled to Microelectrode Arrays (MEAs) is a binding requirement to gather reliable data. Due to physical constraints, low power requirement, or the need of customizability, commercial recording platforms are not fully adequate for the development of experimental setups integrating MEA technology with other equipment needed to perform experiments under climate controlled conditions, like environmental chambers or cell culture incubators. To address this issue, we developed a custom MEA interfacing system featuring low noise, low power, and the capability to be readily integrated inside an incubator-like environment. Two stages, a preamplifier and a filter amplifier, were designed, implemented on printed circuit boards, and tested. The system is characterized by a low input-referred noise (<1 μV RMS), a high channel separation (>70 dB), and signal-to-noise ratio values of neuronal recordings comparable to those obtained with the benchmark commercial MEA system. In addition, the system was successfully integrated with an environmental MEA chamber, without harming cell cultures during experiments and without being damaged by the high humidity level. The devised system is of practical value in the development of in vitro platforms to study temporally extended neuronal network dynamics by means of MEAs.

It is known that cell density influences the maturation process of in vitro neuronal networks. Neuronal cultures plated with different cell densities differ in number of synapses per neuron and thus in single neuron synaptic transmission, which results in a density-dependent neuronal network activity. Although many authors provided detailed information about the effects of cell density on neuronal culture activity, a dedicated report of density and age influence on neuronal hippocampal culture activity has not yet been reported. Therefore, this work aims at providing reference data to researchers that set up an experimental study on hippocampal neuronal cultures, helping in planning and decoding the experiments. In this work, we analysed the effects of both neuronal density and culture age on functional attributes of maturing hippocampal cultures. We characterized the electrophysiological activity of neuronal cultures seeded at three different cell densities, recording their spontaneous electrical activity over maturation by means of MicroElectrode Arrays (MEAs). We had gather data from 86 independent hippocampal cultures to achieve solid statistic results, considering the high culture-to-culture variability. Network activity was evaluated in terms of simple spiking, burst and network burst features. We observed that electrical descriptors were characterized by a functional peak during maturation, followed by a stable phase (for sparse and medium density cultures) or by a decrease phase (for high dense neuronal cultures). Moreover, 900 cells/mm(2) cultures showed characteristics suitable for long lasting experiments (e.g. chronic effect of drug treatments) while 1800 cells/mm(2) cultures should be preferred for experiments that require intense electrical activity (e.g. to evaluate the effect of inhibitory molecules). Finally, cell cultures at 3600 cells/mm(2) are more appropriate for experiments in which time saving is relevant (e.g. drug screenings). These results are intended to be a reference for the planning of in vitro neurophysiological and neuropharmacological experiments with MEAs.