Minimum clinical utility standards for wearable seizure detectors: A simulation study.

OBJECTIVE: Epilepsy management employs self-reported seizure diaries, despite evidence of seizure underreporting. Wearable and implantable seizure detection devices are now becoming more widely available. There are no clear guidelines about what levels of accuracy are sufficient. This study aimed to simulate clinical use cases and identify the necessary level of accuracy for each. METHODS: Using a realistic seizure simulator (CHOCOLATES), a ground truth was produced, which was then sampled to generate signals from simulated seizure detectors of various capabilities. Five use cases were evaluated: (1) randomized clinical trials (RCTs), (2) medication adjustment in clinic, (3) injury prevention, (4) sudden unexpected death in epilepsy (SUDEP) prevention, and (5) treatment of seizure clusters. We considered sensitivity (0%-100%), false alarm rate (FAR; 0-2/day), and device type (external wearable vs. implant) in each scenario. RESULTS: The RCT case was efficient for a wide range of wearable parameters, though implantable devices were preferred. Lower accuracy wearables resulted in subtle changes in the distribution of patients enrolled in RCTs, and therefore higher sensitivity and lower FAR values were preferred. In the clinic case, a wide range of sensitivity, FAR, and device type yielded similar results. For injury prevention, SUDEP prevention, and seizure cluster treatment, each scenario required high sensitivity and yet was minimally influenced by FAR. SIGNIFICANCE: The choice of use case is paramount in determining acceptable accuracy levels for a wearable seizure detection device. We offer simulation results for determining and verifying utility for specific use case and specific wearable parameters.

Imaging for the assessment of the arrhythmogenic potential of mitral valve prolapse.

Mitral valve prolapse (MVP) is the most common valve disease in the western world and recently emerged as a possible substrate for sudden cardiac death (SCD). It is estimated an annual risk of sudden cardiac death of 0.2 to 1.9% mostly caused by complex ventricular arrhythmias (VA). Several mechanisms have been recognized as potentially responsible for arrhythmia onset in MVP, resulting from the combination of morpho-functional abnormality of the mitral valve, structural substrates (regional myocardial hypertrophy, fibrosis, Purkinje fibers activity, inflammation), and mechanical stretch. Echocardiography plays a central role in MVP diagnosis and assessment of severity of regurgitation. Several abnormalities detectable by echocardiography can be prognostic for the occurrence of VA, from morphological alteration including leaflet redundancy and thickness, mitral annular dilatation, and mitral annulus disjunction (MAD), to motion abnormalities detectable with "Pickelhaube" sign. Additionally, speckle-tracking echocardiography may identify MVP patients at higher risk for VA by detection of increased mechanical dispersion. On the other hand, cardiac magnetic resonance (CMR) has the capability to provide a comprehensive risk stratification combining the identification of morphological and motion alteration with the detection of myocardial replacement and interstitial fibrosis, making CMR an ideal method for arrhythmia risk stratification in patients with MVP. Finally, recent studies have suggested a potential role in risk stratification of new techniques such as hybrid PET-MR and late contrast enhancement CT. The purpose of this review is to provide an overview of the mitral valve prolapse syndrome with a focus on the role of imaging in arrhythmic risk stratification. CLINICAL RELEVANCE STATEMENT: Mitral valve prolapse is the most frequent valve condition potentially associated with arrhythmias. Imaging has a central role in the identification of anatomical, functional, mechanical, and structural alterations potentially associated with a higher risk of developing complex ventricular arrhythmia and sudden cardiac death. KEY POINTS: • Mitral valve prolapse is a common valve disease potentially associated with complex ventricular arrhythmia and sudden cardiac death. • The mechanism of arrhythmogenesis in mitral valve prolapse is complex and multifactorial, due to the interplay among multiple conditions including valve morphological alteration, mechanical stretch, myocardial structure remodeling with fibrosis, and inflammation. • Cardiac imaging, especially echocardiography and cardiac magnetic resonance, is crucial in the identification of several features associated with the potential risk of serious cardiac events. In particular, cardiac magnetic resonance has the advantage of being able to detect myocardial fibrosis which is currently the strongest prognosticator.

Myocarditis in anti-synthetase syndrome: clinical features and diagnostic modalities.

OBJECTIVES: Myocarditis is an overlooked manifestation of anti-synthetase syndrome (ASS). Our study describes the clinical and instrumental features of ASS-myocarditis and evaluates the diagnostic performance of cardiac magnetic resonance (CMR) with mapping techniques. METHODS: Data from ASS-patients were retrospectively analyzed. CMR data of patients diagnosed with myocarditis, including late gadolinium enhancement (LGE), T2-ratio, T1-mapping, extra-cellular volume (ECV) and T2-mapping, were reviewed. Myocarditis was defined by the presence of symptoms of heart involvement with increased high-sensitive troponin T (hs-TnT) and/or NT-proBNP and at least an instrumental abnormality. Clinical features of ASS patients with and without myocarditis were compared. A p value<0.05 was considered. RESULTS: Among a cohort of 43 ASS-patients (median age 58[48.0-66.0] years; females 74.4%; anti-Jo1 53.5%), 13(30%) were diagnosed with myocarditis. In 54% of patients, myocarditis was diagnosed at clinical onset. All ASS-myocarditis patients had at least one CMR abnormality: increased ECV in all cases, presence of LGE, increased T1 and T2-mapping in 91%. The 2009-Lake Louis criteria (LLC) were satisfied by 6 patients, the 2018-LLC by 10. With the updated LLC, the sensitivity for myocarditis improved from 54.6% to 91.0%. ASS-patients with myocarditis were more frequently males(53% vs 13%;p=0.009) with fever(69% vs 17%;p=0.001), and had higher hs-TnT (88.0[23.55-311.5] vs 9.80[5.0-23.0]ng/L; p < 0.001), NT-proBNP(525.5[243.5-1575.25] vs 59.0[32.0-165.5;p=0.013]pg/ml;p=0.013) and C-reactive protein(CRP)(7.0[1.7-15.75] vs 1.85[0.5-2.86]mg/L;p=0.011) compared to those without myocarditis. CONCLUSION: In ASS, myocarditis is frequent, even at clinical onset. ASS-patients with myocarditis frequently presented with fever and increased CRP, suggesting the existence of an inflammatory phenotype. The use of novel CMR mapping techniques may increase the diagnostic sensitivity for myocarditis in ASS.

Cardiovascular involvement in Erdheim-Chester diseases is associated with myocardial fibrosis and atrial dysfunction.

PURPOSE: Erdheim-Chester disease (ECD) is a rare multisystem histiocytosis, whose cardiovascular involvement has not been systematically characterized so far. We aimed to systematically (qualitatively and quantitatively) describe the features of cardiovascular involvement in a large cohort of ECD patients and to evaluate its impact on myocardial fibrosis extension and cardiac function. MATERIAL AND METHODS: Among 54 patients with biopsy-proven ECD, 29 patients (59 ± 12 years, 79% males) underwent 1.5-T CMR using a standardized protocol for qualitative and quantitative assessment of disease localization, evaluation of atrial and ventricular function, and assessment of non-dense and dense myocardial fibrosis. RESULTS: The right atrioventricular (AV) groove was the most commonly affected cardiac site (76%) followed by the right atrial walls (63%), thoracic aorta (59%), and superior vena cava (38%). Right AV groove involvement, encasing the right ventricular artery, was associated with non-dense myocardial fibrosis in the infero-septal (20/26 patients) and the inferior (14/26 patients) mid-basal left ventricular (LV) wall. In two patients with right AV groove localization, LGE revealed myocardial infarction in the same myocardial segments. Three out of five patients with left AV groove involvement had non-dense LGE on the lateral LV mid-basal wall. Bulky right atrial pseudomass was associated with atrial dysfunction and superior and inferior vena cava stenosis. CONCLUSIONS: In ECD patients, AV groove localization is associated with LV wall fibrosis in the downstream coronary territories, suggesting hemodynamic alterations due to coronary encasement. Conversely, atrial pseudomass ECD localizations impact on atrial contractility causing atrial dysfunction and are associated with atrio-caval junction stenosis.

Cardiac magnetic resonance findings in acute and post-acute COVID-19 patients with suspected myocarditis.

INTRODUCTION: Cardiac injury is commonly reported in COVID-19 patients, resulting associated to pre-existing cardiovascular disease, disease severity, and unfavorable outcome. Aim is to report cardiac magnetic resonance (CMR) findings in patients with myocarditis-like syndrome during the acute phase of SARS-CoV-2 infection (AMCovS) and post-acute phase (cPACS). METHODS: Between September 2020 and January 2022, 39 consecutive patients (24 males, 58%) were referred to our department to perform a CMR for the suspicion of myocarditis related to AMCovS (n = 17) and cPACS (n = 22) at multimodality evaluation (clinical, laboratory, ECG, and echocardiography). CMR was performed for the assessment of volume, function, edema and fibrosis with standard sequences and mapping techniques. CMR diagnosis and the extension and amount of CMR alterations were recorded. RESULTS: Patients with suspected myocarditis in acute and post-COVID settings were mainly men (10 (59%) and 12 (54.5%), respectively) with older age in AMCovS (58 [48-64]) compared to cPACS (38 [26-53]). Myocarditis was confirmed by CMR in most of cases: 53% of AMCovS and 50% of cPACS with negligible LGE burden (3 [IQR, 1-5] % and 2 [IQR, 1-4] %, respectively). Myocardial infarction was identified in 4/17 (24%) patients with AMCovS. Cardiomyopathies were identified in 12% (3/17) and 27% (6/22) of patients with AMCovS and cPACS, including DCM, HCM and mitral valve prolapse. CONCLUSIONS: In patients with acute and post-acute COVID-19 related suspected myocarditis, CMR improves diagnostic accuracy characterizing ischemic and non-ischemic injury and unraveling subclinical cardiomyopathies.

Data quality evaluation in wearable monitoring.

Wearable recordings of neurophysiological signals captured from the wrist offer enormous potential for seizure monitoring. Yet, data quality remains one of the most challenging factors that impact data reliability. We suggest a combined data quality assessment tool for the evaluation of multimodal wearable data. We analyzed data from patients with epilepsy from four epilepsy centers. Patients wore wristbands recording accelerometry, electrodermal activity, blood volume pulse, and skin temperature. We calculated data completeness and assessed the time the device was worn (on-body), and modality-specific signal quality scores. We included 37,166 h from 632 patients in the inpatient and 90,776 h from 39 patients in the outpatient setting. All modalities were affected by artifacts. Data loss was higher when using data streaming (up to 49% among inpatient cohorts, averaged across respective recordings) as compared to onboard device recording and storage (up to 9%). On-body scores, estimating the percentage of time a device was worn on the body, were consistently high across cohorts (more than 80%). Signal quality of some modalities, based on established indices, was higher at night than during the day. A uniformly reported data quality and multimodal signal quality index is feasible, makes study results more comparable, and contributes to the development of devices and evaluation routines necessary for seizure monitoring.

Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence.

Epilepsy is one of the most common neurological disorders, characterized by the occurrence of repeated seizures. Given that epilepsy is considered a network disorder, tools derived from network neuroscience may confer the valuable ability to quantify the properties of epileptic brain networks. In this study, we use well-established brain network metrics (i.e., mean strength, variance of strength, eigenvector centrality, betweenness centrality) to characterize the temporal evolution of epileptic functional networks over several days prior to seizure occurrence. We infer the networks using long-term electroencephalographic recordings from 12 people with epilepsy. We found that brain network metrics are variable across days and show a circadian periodicity. In addition, we found that in 9 out of 12 patients the distribution of the variance of strength in the day (or even two last days) prior to seizure occurrence is significantly different compared to the corresponding distributions on all previous days. Our results suggest that brain network metrics computed fromelectroencephalographic recordings could potentially be used to characterize brain network changes that occur prior to seizures, and ultimately contribute to seizure warning systems.

Electrodermal activity response during seizures: A systematic review and meta-analysis.

INTRODUCTION: Wearable devices for continuous seizure monitoring have drawn increasing attention in the field of epilepsy research. One of the parameters recorded by these devices is electrodermal activity (EDA). The aim of this study was to systematically review the literature to estimate the incidence of electrodermal response during seizures. METHODS: We searched all articles recording concurrent EDA and EEG activity during the pre-ictal, ictal, and postictal periods in children and adults with epilepsy. Studies reporting the total number of seizures and number of seizures with an EDA response were included for a random-effects meta-analysis. RESULTS: Nineteen studies, including 550 participants and 1115 seizures were reviewed. All studies demonstrated an EDA increase during the ictal and postictal periods, while only three reported pre-ictal EDA responses. The meta-analysis showed a pooled EDA response incidence of 82/100 seizures (95% CI 70-91). Tonic-clonic seizures (both generalized tonic-clonic seizures (GTCS) and focal to bilateral tonic-clonic seizures (FBTCS)) elicited a more pronounced (higher and longer-lasting) EDA response when compared with focal seizures (excluding FBTCS). DISCUSSION: Epileptic seizures produce an electrodermal response detectable by wearable devices during the pre-ictal, ictal, and postictal periods. Further research is needed to better understand EDA changes and to analyze factors which may influence the EDA response.

Diagnostic yield and limitations of in-hospital documentation in patients with epilepsy.

OBJECTIVE: To determine the diagnostic yield of in-hospital video-electroencephalography (EEG) monitoring to document seizures in patients with epilepsy. METHODS: Retrospective analysis of electronic seizure documentation at the University Hospital Freiburg (UKF) and at King's College London (KCL). Statistical assessment of the role of the duration of monitoring, and subanalyses on presurgical patient groups and patients undergoing reduction of antiseizure medication. RESULTS: Of more than 4800 patients with epilepsy undergoing in-hospital recordings at the two institutions since 2005, seizures with documented for 43% (KCL) and 73% (UKF).. Duration of monitoring was highly significantly associated with seizure recordings (p < .0001), and presurgical patients as well as patients with drug reduction had a significantly higher diagnostic yield (p < .0001). Recordings with a duration of >5 days lead to additional new seizure documentation in only less than 10% of patients. SIGNIFICANCE: There is a need for the development of new ambulatory monitoring strategies to document seizures for diagnostic and monitoring purposes for a relevant subgroup of patients with epilepsy in whom in-hospital monitoring fails to document seizures.

Intra- and Inter-Subject Perspectives on the Detection of Focal Onset Motor Seizures in Epilepsy Patients.

Focal onset epileptic seizures are highly heterogeneous in their clinical manifestations, and a robust seizure detection across patient cohorts has to date not been achieved. Here, we assess and discuss the potential of supervised machine learning models for the detection of focal onset motor seizures by means of a wrist-worn wearable device, both in a personalized context as well as across patients. Wearable data were recorded in-hospital from patients with epilepsy at two epilepsy centers. Accelerometry, electrodermal activity, and blood volume pulse data were processed and features for each of the biosignal modalities were calculated. Following a leave-one-out approach, a gradient tree boosting machine learning model was optimized and tested in an intra-subject and inter-subject evaluation. In total, 20 seizures from 9 patients were included and we report sensitivities of 67% to 100% and false alarm rates of down to 0.85 per 24 h in the individualized assessment. Conversely, for an inter-subject seizure detection methodology tested on an out-of-sample data set, an optimized model could only achieve a sensitivity of 75% at a false alarm rate of 13.4 per 24 h. We demonstrate that robustly detecting focal onset motor seizures with tonic or clonic movements from wearable data may be possible for individuals, depending on specific seizure manifestations.

Noninvasive mobile EEG as a tool for seizure monitoring and management: A systematic review.

In the last two decades new noninvasive mobile electroencephalography (EEG) solutions have been developed to overcome limitations of conventional clinical EEG and to improve monitoring of patients with long-term conditions. Despite the availability of mobile innovations, their adoption is still very limited. The aim of this study is to review the current state-of-the-art and highlight the main advantages of adopting noninvasive mobile EEG solutions in clinical trials and research studies of people with epilepsy or suspected seizures. Device characteristics are described, and their evaluation is presented. Two authors independently performed a literature review in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A combination of different digital libraries was used (Embase, MEDLINE, Global Health, PsycINFO and https://clinicaltrials.gov/). Twenty-three full-text, six conference abstracts, and eight webpages were included, where a total of 14 noninvasive mobile solutions were identified. Published studies demonstrated at different levels how EEG recorded via mobile EEG can be used for visual detection of EEG abnormalities and for the application of automatic-detection algorithms with acceptable specificity and sensitivity. When the quality of the signal was compared with scalp EEG, many similarities were found in the background activities and power spectrum. Several studies indicated that the experience of patients and health care providers using mobile EEG was positive in different settings. Ongoing trials are focused mostly on improving seizure-detection accuracy and also on testing and assessing feasibility and acceptability of noninvasive devices in the hospital and at home. This review supports the potential clinical value of noninvasive mobile EEG systems and their advantages in terms of time, technical support, cost, usability, and reliability when applied to seizure detection and management. On the other hand, the limitations of the studies confirmed that future research is needed to provide more evidence regarding feasibility and acceptability in different settings, as well as the data quality and detection accuracy of new noninvasive mobile EEG solutions.

Cardiac magnetic resonance in systemic sclerosis myocarditis: the value of T2 mapping to detect myocardial inflammation.

OBJECTIVES: Myocarditis in SSc is associated with a poor prognosis. Cardiac magnetic resonance (CMR) is the non-invasive diagnostic modality of choice for SSc myocarditis. Our study investigates the performance of the mapping techniques included in the revised Lake Louise criteria (LLC) for the identification of SSc myocarditis. METHODS: CMR data (right and left ventricular function and morphology, early and late gadolinium enhancement [LGE], T2 ratio, and T1 mapping, extracellular volume [ECV] and T2 mapping) of SSc patients diagnosed with myocarditis were reviewed. Myocarditis was defined by the presence of symptoms of SSc heart involvement with increased high-sensitive troponin T (hs-TnT) and/or NT-proBNP and at least an abnormality at 24 h ECG Holter and/or echocardiography and/or CMR. A P-value < 0.05 was considered as statistically significant. RESULTS: Nineteen patients (median age 54 [46-70] years; females 78.9%; diffuse SSc 52.6%; anti-Scl70+ 52.6%) were identified: 11 (57.9%) had echocardiographic, and 8 (42.8%) 24 h ECG Holter abnormalities. All patients had at least one CMR abnormality: LGE in 18 (94.7%), increased ECV in 10 (52.6%) and T2 mapping >50 ms in 15 (78.9%). Median T1 and T2 mapping were 1085 [1069-1110] ms and 53.1 [52-54] ms, respectively. T1 mapping directly correlated with NT-proBNP (r = 0.620; P = 0.005), ESR (r = 0.601; P = 0.008), CRP (r = 0.685; P = 0.001) and skin score (r = 0.507; P = 0.027); ECV correlated with NT-proBNP serum levels (r = 0.702; P = 0.001). No correlations emerged between T2 mapping and other parameters. Ten patients satisfied the 2009 LLC, 17 the 2018 LLC. With the new criteria including T2 mapping, the sensitivity improved from 52.6% to 89.5%. CONCLUSION: The CMR mapping techniques improve the sensitivity to detect myocardial inflammation in patients with SSc heart involvement. The evaluation of T2 mapping increases diagnostic accuracy for the recognition of myocardial inflammation in SSc.

Detecting Tonic-Clonic Seizures in Multimodal Biosignal Data From Wearables: Methodology Design and Validation.

BACKGROUND: Video electroencephalography recordings, routinely used in epilepsy monitoring units, are the gold standard for monitoring epileptic seizures. However, monitoring is also needed in the day-to-day lives of people with epilepsy, where video electroencephalography is not feasible. Wearables could fill this gap by providing patients with an accurate log of their seizures. OBJECTIVE: Although there are already systems available that provide promising results for the detection of tonic-clonic seizures (TCSs), research in this area is often limited to detection from 1 biosignal modality or only during the night when the patient is in bed. The aim of this study is to provide evidence that supervised machine learning can detect TCSs from multimodal data in a new data set during daytime and nighttime. METHODS: An extensive data set of biosignals from a multimodal watch worn by people with epilepsy was recorded during their stay in the epilepsy monitoring unit at 2 European clinical sites. From a larger data set of 243 enrolled participants, those who had data recorded during TCSs were selected, amounting to 10 participants with 21 TCSs. Accelerometry and electrodermal activity recorded by the wearable device were used for analysis, and seizure manifestation was annotated in detail by clinical experts. Ten accelerometry and 3 electrodermal activity features were calculated for sliding windows of variable size across the data. A gradient tree boosting algorithm was used for seizure detection, and the optimal parameter combination was determined in a leave-one-participant-out cross-validation on a training set of 10 seizures from 8 participants. The model was then evaluated on an out-of-sample test set of 11 seizures from the remaining 2 participants. To assess specificity, we additionally analyzed data from up to 29 participants without TCSs during the model evaluation. RESULTS: In the leave-one-participant-out cross-validation, the model optimized for sensitivity could detect all 10 seizures with a false alarm rate of 0.46 per day in 17.3 days of data. In a test set of 11 out-of-sample TCSs, amounting to 8.3 days of data, the model could detect 10 seizures and produced no false positives. Increasing the test set to include data from 28 more participants without additional TCSs resulted in a false alarm rate of 0.19 per day in 78 days of wearable data. CONCLUSIONS: We show that a gradient tree boosting machine can robustly detect TCSs from multimodal wearable data in an original data set and that even with very limited training data, supervised machine learning can achieve a high sensitivity and low false-positive rate. This methodology may offer a promising way to approach wearable-based nonconvulsive seizure detection.

Wearable devices for seizure detection: Practical experiences and recommendations from the Wearables for Epilepsy And Research (WEAR) International Study Group.

The Wearables for Epilepsy And Research (WEAR) International Study Group identified a set of methodology standards to guide research on wearable devices for seizure detection. We formed an international consortium of experts from clinical research, engineering, computer science, and data analytics at the beginning of 2020. The study protocols and practical experience acquired during the development of wearable research studies were discussed and analyzed during bi-weekly virtual meetings to highlight commonalities, strengths, and weaknesses, and to formulate recommendations. Seven major essential components of the experimental design were identified, and recommendations were formulated about: (1) description of study aims, (2) policies and agreements, (3) study population, (4) data collection and technical infrastructure, (5) devices, (6) reporting results, and (7) data sharing. Introducing a framework of methodology standards promotes optimal, accurate, and consistent data collection. It also guarantees that studies are generalizable and comparable, and that results can be replicated, validated, and shared.

Digital semiology and time-evolution pattern of bio-signals in focal onset motor seizures.

PURPOSE: Focal seizures constitute the most common seizure type and are associated with poor control. One of the major difficulties in detecting focal onset with wearable devices seizures is related to their phenomenological complexity. We aimed at capturing focal onset seizures with motor manifestations with a multimodal wearable device to identify the digital semiology and the evolution pattern of ictal manifestations. METHODS: Participants were asked to wear a multimodal wearable device (IMEC) aimed at seizure detection while admitted to an epilepsy monitoring unit. Seizures were labelled by a neurologist and start and offset time were noted. The signals captured by the device during the seizure window were plotted and a visual inspection was performed for focal motor seizures with impaired awareness and for focal motor aware seizures. RESULTS: Fifty-three seizures from twelve patients with focal seizures with motor manifestations recorded with the device were visually inspected. Overall, a common pattern presented across focal motor seizures with impaired awareness and it was characterized by early cardiac manifestations followed by motor phenomena and final EDA response. Motor seizures with retained awareness appeared to be characterized by brief motor events not associated with major autonomic manifestations Conclusion: an overall common digital phenotype and time-evolution pattern was demonstrated for focal motor seizures with impaired awareness. The identification of the evolution pattern could more precisely inform the development of highly preforming algorithms opening the possibility to a more precise, and potentially customizable way to optimize focal seizure detection.

Remote and Long-Term Self-Monitoring of Electroencephalographic and Noninvasive Measurable Variables at Home in Patients With Epilepsy (EEG@HOME): Protocol for an Observational Study.

BACKGROUND: Epileptic seizures are spontaneous events that severely affect the lives of patients due to their recurrence and unpredictability. The integration of new wearable and mobile technologies to collect electroencephalographic (EEG) and extracerebral signals in a portable system might be the solution to prospectively identify times of seizure occurrence or propensity. The performances of several seizure detection devices have been assessed by validated studies, and patient perspectives on wearables have been explored to better match their needs. Despite this, there is a major gap in the literature on long-term, real-life acceptability and performance of mobile technology essential to managing chronic disorders such as epilepsy. OBJECTIVE: EEG@HOME is an observational, nonrandomized, noninterventional study that aims to develop a new feasible procedure that allows people with epilepsy to independently, continuously, and safely acquire noninvasive variables at home. The data collected will be analyzed to develop a general model to predict periods of increased seizure risk. METHODS: A total of 12 adults with a diagnosis of pharmaco-resistant epilepsy and at least 20 seizures per year will be recruited at King's College Hospital, London. Participants will be asked to self-apply an easy and portable EEG recording system (ANT Neuro) to record scalp EEG at home twice daily. From each serial EEG recording, brain network ictogenicity (BNI), a new biomarker of the propensity of the brain to develop seizures, will be extracted. A noninvasive wrist-worn device (Fitbit Charge 3; Fitbit Inc) will be used to collect non-EEG biosignals (heart rate, sleep quality index, and steps), and a smartphone app (Seer app; Seer Medical) will be used to collect data related to seizure occurrence, medication taken, sleep quality, stress, and mood. All data will be collected continuously for 6 months. Standardized questionnaires (the Post-Study System Usability Questionnaire and System Usability Scale) will be completed to assess the acceptability and feasibility of the procedure. BNI, continuous wrist-worn sensor biosignals, and electronic survey data will be correlated with seizure occurrence as reported in the diary to investigate their potential values as biomarkers of seizure risk. RESULTS: The EEG@HOME project received funding from Epilepsy Research UK in 2018 and was approved by the Bromley Research Ethics Committee in March 2020. The first participants were enrolled in October 2020, and we expect to publish the first results by the end of 2022. CONCLUSIONS: With the EEG@HOME study, we aim to take advantage of new advances in remote monitoring technology, including self-applied EEG, to investigate the feasibility of long-term disease self-monitoring. Further, we hope our study will bring new insights into noninvasively collected personalized risk factors of seizure occurrence and seizure propensity that may help to mitigate one of the most difficult aspects of refractory epilepsy: the unpredictability of seizure occurrence. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/25309.

Remote Assessment of Disease and Relapse in Epilepsy: Protocol for a Multicenter Prospective Cohort Study.

BACKGROUND: In recent years, a growing body of literature has highlighted the role of wearable and mobile remote measurement technology (RMT) applied to seizure detection in hospital settings, whereas more limited evidence has been produced in the community setting. In clinical practice, seizure assessment typically relies on self-report, which is known to be highly unreliable. Moreover, most people with epilepsy self-identify factors that lead to increased seizure likelihood, including mood, behavior, sleep pattern, and cognitive alterations, all of which are amenable to measurement via multiparametric RMT. OBJECTIVE: The primary aim of this multicenter prospective cohort study is to assess the usability, feasibility, and acceptability of RMT in the community setting. In addition, this study aims to determine whether multiparametric RMT collected in populations with epilepsy can prospectively estimate variations in seizure occurrence and other outcomes, including seizure frequency, quality of life, and comorbidities. METHODS: People with a diagnosis of pharmacoresistant epilepsy will be recruited in London, United Kingdom, and Freiburg, Germany. Participants will be asked to wear a wrist-worn device and download ad hoc apps developed on their smartphones. The apps will be used to collect data related to sleep, physical activity, stress, mood, social interaction, speech patterns, and cognitive function, both passively from existing smartphone sensors (passive remote measurement technology [pRMT]) and actively via questionnaires, tasks, and assessments (active remote measurement technology [aRMT]). Data will be collected continuously for 6 months and streamed to the Remote Assessment of Disease and Relapse-base (RADAR-base) server. RESULTS: The RADAR Central Nervous System project received funding in 2015 from the Innovative Medicines Initiative 2 Joint Undertaking under Grant Agreement No. 115902. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation program and European Federation of Pharmaceutical Industries and Associations. Ethical approval was obtained in London from the Bromley Research Ethics Committee (research ethics committee reference: 19/LO/1884) in January 2020. The first participant was enrolled on September 30, 2020. Data will be collected until September 30, 2021. The results are expected to be published at the beginning of 2022. CONCLUSIONS: RADAR Epilepsy aims at developing a framework of continuous data collection intended to identify ictal and preictal states through the use of aRMT and pRMT in the real-life environment. The study was specifically designed to evaluate the clinical usefulness of the data collected via new technologies and compliance, technology acceptability, and usability for patients. These are key aspects to successful adoption and implementation of RMT as a new way to measure and manage long-term disorders. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/21840.

Day and night comfort and stability on the body of four wearable devices for seizure detection: A direct user-experience.

PURPOSE: Wearable devices are progressively becoming an available tool for continuous seizure detection. Motivation to use wearables is not only driven by the accuracy and reliability of the performance but also by the form factor, comfort, and stability on the body. We collected direct feedback and device placement-related issues experienced by a cohort of people with epilepsy (PWE) to investigate to what extent available devices are nonintrusive, comfortable, and stable on the body. METHODS: Four models of wearable devices (E4 wrist band, Everion upper arm band, IMEC upper arm band, and Epilog scalp patch electrodes) were worn by PWE who were admitted to two epilepsy monitoring units (EMUs) in London and Freiburg. Participants were periodically reviewed, and accidental displacements of the devices were annotated. Participants' experience was assessed using the Technology Acceptance Model Fast Form (TAM-FF) plus two additional questions on comfort. A thematic analysis was also performed on the free text of the questionnaire. RESULTS: One hundred and fifteen participants were enrolled. The devices had a good stability on the body including during seizures. Overall, all the devices were considered comfortable to be worn, including during sleep. However, devices containing wires and patches demonstrated a lesser degree of stability on the body and were judged less positively. Participants age was correlated with TAM-FF mean scores, and older participants judged the devices less favorably compared with younger participants. DISCUSSION: Removable but securely fitted, wireless, and comfortable designs were considered more appropriate for a continuous monitoring aimed at seizure detection. Some caution may be required when patch electrodes and electrodes glued to the skin or to the scalp are used, as those evaluated in the present study demonstrated a lower level of acceptability and a lower degree of stability to the body, especially at night. These factors could limit a continuous monitoring decreasing the device performance for nocturnal, unsupervised seizures which are at higher risk of lethality.

Postictal generalized EEG suppression and postictal immobility: what do we know?

Despite representing the leading cause of epilepsy-related mortality, the pathophysiology of sudden unexpected death in epilepsy (SUDEP) remains elusive. In this context, the identification of clinical markers of SUDEP assumes a great importance and has been the target of many studies aimed at stratifying patients' individual risk. Among the potentially most hazardous post-ictal phenomena observed following convulsive seizures in monitored SUDEP cases, postictal generalized EEG suppression and postictal immobility have attracted attention as potential SUDEP risk factors. In this manuscript, we review the current knowledge on postictal generalized EEG suppression and postictal immobility, aiming to identify their pathophysiological mechanisms, reported frequencies and associated clinical factors, and critically evaluate the evidence on their potential relevance as SUDEP risk markers.

Signal quality and patient experience with wearable devices for epilepsy management.

Noninvasive wearable devices have great potential to aid the management of epilepsy, but these devices must have robust signal quality, and patients must be willing to wear them for long periods of time. Automated machine learning classification of wearable biosensor signals requires quantitative measures of signal quality to automatically reject poor-quality or corrupt data segments. In this study, commercially available wearable sensors were placed on patients with epilepsy undergoing in-hospital or in-home electroencephalographic (EEG) monitoring, and healthy volunteers. Empatica E4 and Biovotion Everion were used to record accelerometry (ACC), photoplethysmography (PPG), and electrodermal activity (EDA). Byteflies Sensor Dots were used to record ACC and PPG, the Activinsights GENEActiv watch to record ACC, and Epitel Epilog to record EEG data. PPG and EDA signals were recorded for multiple days, then epochs of high-quality, marginal-quality, or poor-quality data were visually identified by reviewers, and reviewer annotations were compared to automated signal quality measures. For ACC, the ratio of spectral power from 0.8 to 5 Hz to broadband power was used to separate good-quality signals from noise. For EDA, the rate of amplitude change and prevalence of sharp peaks significantly differentiated between good-quality data and noise. Spectral entropy was used to assess PPG and showed significant differences between good-, marginal-, and poor-quality signals. EEG data were evaluated using methods to identify a spectral noise cutoff frequency. Patients were asked to rate the usability and comfort of each device in several categories. Patients showed a significant preference for the wrist-worn devices, and the Empatica E4 device was preferred most often. Current wearable devices can provide high-quality data and are acceptable for routine use, but continued development is needed to improve data quality, consistency, and management, as well as acceptability to patients.