Signal quality and patient experience with wearable devices for epilepsy management

User experience of a seizure risk forecasting app: A mixed methods investigation

OBJECTIVE

Over recent years, there has been a growing interest in exploring the utility of seizure risk forecasting, particularly how it could improve quality of life for people living with epilepsy. This study reports on user experiences and perspectives of a seizure risk forecaster app, as well as the potential impact on mood and adjustment to epilepsy.

METHODS

Active app users were asked to complete a survey (baseline and 3-month follow-up) to assess perspectives on the forecast feature as well as mood and adjustment. Post-hoc, nine neutral forecast users (neither agreed nor disagreed it was useful) completed semi-structured interviews, to gain further insight into their perspectives of epilepsy management and seizure forecasting. Non-parametric statistical tests and inductive thematic analyses were used to analyse the quantitative and qualitative data, respectively.

RESULTS

Surveys were completed by 111 users. Responders consisted of "app users" (n = 58), and "app and forecast users" (n = 53). Of the "app and forecast users", 40 % believed the forecast was accurate enough to be useful in monitoring for seizure risk, and 60 % adopted it for purposes like scheduling activities and helping mental state. Feeling more in control was the most common response to both high and low risk forecasted states. In-depth interviews revealed five broad themes, of which 'frustrations with lack of direction' (regarding their current epilepsy management approach), 'benefits of increased self-knowledge' and 'current and anticipated usefulness of forecasting' were the most common.

SIGNIFICANCE

Preliminary results suggest that seizure risk forecasting can be a useful tool for people with epilepsy to make lifestyle changes, such as scheduling daily events, and experience greater feelings of control. These improvements may be attributed, at least partly, to the improvements in self-knowledge experienced through forecast use.

Feasibility and acceptability of an ultra-long-term at-home EEG monitoring system (EEG@HOME) for people with epilepsy

BACKGROUND

Recent technological advancements offer new ways to monitor and manage epilepsy. The adoption of these devices in routine clinical practice will strongly depend on patient acceptability and usability, with their perspectives being crucial. Previous studies provided feedback from patients, but few explored the experience of them using independently multiple devices independently at home.

PURPOSE

The study, assessed through a mixed methods design, the direct experiences of people with epilepsy independently using a non-invasive monitoring system (EEG@HOME) for an extended duration of 6 months, at home. We aimed to investigate factors affecting engagement, gather qualitative insights, and provide recommendations for future home epilepsy monitoring systems.

MATERIALS AND METHODS

Adults with epilepsy independently were trained to use a wearable dry EEG system, a wrist-worn device, and a smartphone app for seizure tracking and behaviour monitoring for 6 months at home. Monthly acceptability questionnaires (PSSUQ, SUS) and semi-structured interviews were conducted to explore participant experience. Adherence with the procedure, acceptability scores and systematic thematic analysis of the interviews, focusing on the experience with the procedure, motivation and benefits and opinion about the procedure were assessed.

RESULTS

Twelve people with epilepsy took part into the study for an average of 193.8 days (range 61 to 312) with a likelihood of using the system at six months of 83 %. The e-diary and the smartwatch were highly acceptable and preferred to a wearable EEG system (PSSUQ score of 1.9, 1.9, 2.4). Participants showed an acceptable level of adherence with all solutions (Average usage of 63 %, 66 %, 92 %) reporting more difficulties using the EEG twice a day and remembering to complete the daily behavioural questionnaires. Clear information and training, continuous remote support, perceived direct and indirect benefits and the possibility to have a flexible, tailored to daily routine monitoring were defined as key factors to ensure compliance with long-term monitoring systems.

CONCLUSIONS

EEG@HOME study demonstrated people with epilepsy' interest and ability in active health monitoring using new technologies. Remote training and support enable independent home use of new non-invasive technologies, but to ensure long term acceptability and usability systems will require to be integrated into patients' routines, include healthcare providers, and offer continuous support and personalized feedback.

A real-world dataset of group emotion experiences based on physiological data

Affective computing has experienced substantial advancements in recognizing emotions through image and facial expression analysis. However, the incorporation of physiological data remains constrained. Emotion recognition with physiological data shows promising results in controlled experiments but lacks generalization to real-world settings. To address this, we present G-REx, a dataset for real-world affective computing. We collected physiological data (photoplethysmography and electrodermal activity) using a wrist-worn device during long-duration movie sessions. Emotion annotations were retrospectively performed on segments with elevated physiological responses. The dataset includes over 31 movie sessions, totaling 380 h+ of data from 190+ subjects. The data were collected in a group setting, which can give further context to emotion recognition systems. Our setup aims to be easily replicable in any real-life scenario, facilitating the collection of large datasets for novel affective computing systems.

Benchmarking of Sensor Configurations and Measurement Sites for Out-of-the-Lab Photoplethysmography

Photoplethysmography (PPG) is used for heart-rate monitoring in a variety of contexts and applications due to its versatility and simplicity. These applications, namely studies involving PPG data acquisition during day-to-day activities, require reliable and continuous measurements, which are often performed at the index finger or wrist. However, some PPG sensors are susceptible to saturation, motion artifacts, and discomfort upon their use. In this paper, an off-the-shelf PPG sensor was benchmarked and modified to improve signal saturation. Moreover, this paper explores the feasibility of using an optimized sensor in the lower limb as an alternative measurement site. Data were collected from 28 subjects with ages ranging from 18 to 59 years. To validate the sensors' performance, signal saturation and quality, wave morphology, performance of automatic systolic peak detection, and heart-rate estimation, were compared. For the upper and lower limb locations, the index finger and the first toe were used as reference locations, respectively. Lowering the amplification stage of the PPG sensor resulted in a significant reduction in signal saturation, from 18% to 0.5%. Systolic peak detection at rest using an automatic algorithm showed a sensitivity and precision of 0.99 each. The posterior wrist and upper arm showed pulse wave morphology correlations of 0.93 and 0.92, respectively. For these locations, peak detection sensitivity and precision were 0.95, 0.94 and 0.89, 0.89, respectively. Overall, the adjusted PPG sensors are a good alternative for obtaining high-quality signals at the fingertips, and for new measurement sites, the posterior pulse and the upper arm allow for high-quality signal extraction.

Seizure forecasting: Bifurcations in the long and winding road

To date, the unpredictability of seizures remains a source of suffering for people with epilepsy, motivating decades of research into methods to forecast seizures. Originally, only few scientists and neurologists ventured into this niche endeavor, which, given the difficulty of the task, soon turned into a long and winding road. Over the past decade, however, our narrow field has seen a major acceleration, with trials of chronic electroencephalographic devices and the subsequent discovery of cyclical patterns in the occurrence of seizures. Now, a burgeoning science of seizure timing is emerging, which in turn informs best forecasting strategies for upcoming clinical trials. Although the finish line might be in view, many challenges remain to make seizure forecasting a reality. This review covers the most recent scientific, technical, and medical developments, discusses methodology in detail, and sets a number of goals for future studies.

Development of a Multivariable Seizure Likelihood Assessment Based on Clinical Information and Short Autonomic Activity Recordings for Children With Epilepsy

BACKGROUND

Predicting seizure likelihood for the following day would enable clinicians to extend or potentially schedule video-electroencephalography (EEG) monitoring when seizure risk is high. Combining standardized clinical data with short-term recordings of wearables to predict seizure likelihood could have high practical relevance as wearable data is easy and fast to collect. As a first step toward seizure forecasting, we classified patients based on whether they had seizures or not during the following recording.

METHODS

Pediatric patients admitted to the epilepsy monitoring unit wore a wearable that recorded the heart rate (HR), heart rate variability (HRV), electrodermal activity (EDA), and peripheral body temperature. We utilized short recordings from 9:00 to 9:15 pm and compared mean values between patients with and without impending seizures. In addition, we collected clinical data: age, sex, age at first seizure, generalized slowing, focal slowing, and spikes on EEG, magnetic resonance imaging findings, and antiseizure medication reduction. We used conventional machine learning techniques with cross-validation to classify patients with and without impending seizures.

RESULTS

We included 139 patients: 78 had no seizures and 61 had at least one seizure after 9 pm during the concurrent video-EEG and E4 recordings. HR (P < 0.01) and EDA (P < 0.01) were lower and HRV (P = 0.02) was higher for patients with than for patients without impending seizures. The average accuracy of group classification was 66%, and the mean area under the receiver operating characteristics was 0.72.

CONCLUSIONS

Short-term wearable recordings in combination with clinical data have great potential as an easy-to-use seizure likelihood assessment tool.

Analyzing physiological signals recorded with a wearable sensor across the menstrual cycle using circular statistics

This study aims to identify the most significant features in physiological signals representing a biphasic pattern in the menstrual cycle using circular statistics which is an appropriate analytic method for the interpretation of data with a periodic nature. The results can be used empirically to determine menstrual phases. A non-uniform pattern was observed in ovulating subjects, with a significant periodicity (p< 0.05) in mean temperature, heart rate (HR), Inter-beat Interval (IBI), mean tonic component of Electrodermal Activity (EDA), and signal magnitude area (SMA) of the EDA phasic component in the frequency domain. In contrast, non-ovulating cycles displayed a more uniform distribution (p> 0.05). There was a significant difference between ovulating and non-ovulating cycles (p< 0.05) in temperature, IBI, and EDA but not in mean HR. Selected features were used in training an Autoregressive Integrated Moving Average (ARIMA) model, using data from at least one cycle of a subject, to predict the behavior of the signal in the last cycle. By iteratively retraining the algorithm on a per-day basis, the mean temperature, HR, IBI and EDA tonic values of the next day were predicted with root mean square error (RMSE) of 0.13 ± 0.07 (C°), 1.31 ± 0.34 (bpm), 0.016 ± 0.005 (s) and 0.17 ± 0.17 (μS), respectively.

PreEpiSeizures: description and outcomes of physiological data acquisition using wearable devices during video-EEG monitoring in people with epilepsy

The PreEpiSeizures project was created to better understand epilepsy and seizures through wearable technologies. The motivation was to capture physiological information related to epileptic seizures, besides Electroencephalography (EEG) during video-EEG monitorings. If other physiological signals have reliable information of epileptic seizures, unobtrusive wearable technology could be used to monitor epilepsy in daily life. The development of wearable solutions for epilepsy is limited by the nonexistence of datasets which could validate these solutions. Three different form factors were developed and deployed, and the signal quality was assessed for all acquired biosignals. The wearable data acquisition was performed during the video-EEG of patients with epilepsy. The results achieved so far include 59 patients from 2 hospitals totaling 2,721 h of wearable data and 348 seizures. Besides the wearable data, the Electrocardiogram of the hospital is also useable, totalling 5,838 h of hospital data. The quality ECG signals collected with the proposed wearable is equated with the hospital system, and all other biosignals also achieved state-of-the-art quality. During the data acquisition, 18 challenges were identified, and are presented alongside their possible solutions. Though this is an ongoing work, there were many lessons learned which could help to predict possible problems in wearable data collections and also contribute to the epilepsy community with new physiological information. This work contributes with original wearable data and results relevant to epilepsy research, and discusses relevant challenges that impact wearable health monitoring.

Wearable Technologies for Electrodermal and Cardiac Activity Measurements: A Comparison between Fitbit Sense, Empatica E4 and Shimmer GSR3

The capability of measuring specific neurophysiological and autonomic parameters plays a crucial role in the objective evaluation of a human's mental and emotional states. These human aspects are commonly known in the scientific literature to be involved in a wide range of processes, such as stress and arousal. These aspects represent a relevant factor especially in real and operational environments. Neurophysiological autonomic parameters, such as Electrodermal Activity (EDA) and Photoplethysmographic data (PPG), have been usually investigated through research-graded devices, therefore resulting in a high degree of invasiveness, which could negatively interfere with the monitored user's activity. For such a reason, in the last decade, recent consumer-grade wearable devices, usually designed for fitness-tracking purposes, are receiving increasing attention from the scientific community, and are characterized by a higher comfort, ease of use and, therefore, by a higher compatibility with daily-life environments. The present preliminary study was aimed at assessing the reliability of a consumer wearable device, i.e., the Fitbit Sense, with respect to a research-graded wearable, i.e., the Empatica E4 wristband, and a laboratory device, i.e., the Shimmer GSR3+. EDA and PPG data were collected among 12 participants while they performed multiple resting conditions. The results demonstrated that the EDA- and PPG-derived features computed through the wearable and research devices were positively and significantly correlated, while the reliability of the consumer device was significantly lower.

Seizure occurrence is linked to multiday cycles in diverse physiological signals

OBJECTIVE

The factors that influence seizure timing are poorly understood, and seizure unpredictability remains a major cause of disability. Work in chronobiology has shown that cyclical physiological phenomena are ubiquitous, with daily and multiday cycles evident in immune, endocrine, metabolic, neurological, and cardiovascular function. Additionally, work with chronic brain recordings has identified that seizure risk is linked to daily and multiday cycles in brain activity. Here, we provide the first characterization of the relationships between the cyclical modulation of a diverse set of physiological signals, brain activity, and seizure timing.

METHODS

In this cohort study, 14 subjects underwent chronic ambulatory monitoring with a multimodal wrist-worn sensor (recording heart rate, accelerometry, electrodermal activity, and temperature) and an implanted responsive neurostimulation system (recording interictal epileptiform abnormalities and electrographic seizures). Wavelet and filter-Hilbert spectral analyses characterized circadian and multiday cycles in brain and wearable recordings. Circular statistics assessed electrographic seizure timing and cycles in physiology.

RESULTS

Ten subjects met inclusion criteria. The mean recording duration was 232 days. Seven subjects had reliable electroencephalographic seizure detections (mean = 76 seizures). Multiday cycles were present in all wearable device signals across all subjects. Seizure timing was phase locked to multiday cycles in five (temperature), four (heart rate, phasic electrodermal activity), and three (accelerometry, heart rate variability, tonic electrodermal activity) subjects. Notably, after regression of behavioral covariates from heart rate, six of seven subjects had seizure phase locking to the residual heart rate signal.

SIGNIFICANCE

Seizure timing is associated with daily and multiday cycles in multiple physiological processes. Chronic multimodal wearable device recordings can situate rare paroxysmal events, like seizures, within a broader chronobiology context of the individual. Wearable devices may advance the understanding of factors that influence seizure risk and enable personalized time-varying approaches to epilepsy care.

Behavioural Models of Risk-Taking in Human-Robot Tactile Interactions

Touch can have a strong effect on interactions between people, and as such, it is expected to be important to the interactions people have with robots. In an earlier work, we showed that the intensity of tactile interaction with a robot can change how much people are willing to take risks. This study further develops our understanding of the relationship between human risk-taking behaviour, the physiological responses by the user, and the intensity of the tactile interaction with a social robot. We used data collected with physiological sensors during the playing of a risk-taking game (the Balloon Analogue Risk Task, or BART). The results of a mixed-effects model were used as a baseline to predict risk-taking propensity from physiological measures, and these results were further improved through the use of two machine learning techniques-support vector regression (SVR) and multi-input convolutional multihead attention (MCMA)-to achieve low-latency risk-taking behaviour prediction during human-robot tactile interaction. The performance of the models was evaluated based on mean absolute error (MAE), root mean squared error (RMSE), and R squared score (R2), which obtained the optimal result with MCMA yielding an MAE of 3.17, an RMSE of 4.38, and an R2 of 0.93 compared with the baseline of 10.97 MAE, 14.73 RMSE, and 0.30 R2. The results of this study offer new insights into the interplay between physiological data and the intensity of risk-taking behaviour in predicting human risk-taking behaviour during human-robot tactile interactions. This work illustrates that physiological activation and the intensity of tactile interaction play a prominent role in risk processing during human-robot tactile interaction and demonstrates that it is feasible to use human physiological data and behavioural data to predict risk-taking behaviour in human-robot tactile interaction.

Perceived seizure risk in epilepsy â€" Chronic electronic surveys with and without concurrent EEG

Objective

Previous studies suggested that patients with epilepsy might be able to fore-cast their own seizures. We sought to assess the relationships of premonitory symptoms and perceived seizure risk with future and recent self-reported and EEG-confirmed seizures in the subjects living with epilepsy in their natural home environments.

Methods

We collected long-term e-surveys from ambulatory patients with and without concurrent EEG recordings. Information obtained from the e-surveys included medication compliance, sleep quality, mood, stress, perceived seizure risk and seizure occurrences preceding the survey. EEG seizures were identified. Univariate and multivariate generalized linear mixed-effect regression models were used to estimate odds ratios (ORs) for the assessment of the relationships. Results were compared with device seizure forecasting literature using a mathematical formula converting OR to equivalent area under the curve (AUC).

Results

Sixty-nine subjects returned 12,590 e-survey entries, with four subjects acquiring concurrent EEG recordings. Univariate analysis revealed increased stress (OR = 2.52, 95% CI = [1.52, 4.14], p < 0.001) and decreased mood (0.32, [0.13, 0.82], 0.02) were associated with increased relative odds of future self-reported seizures. On multivariate analysis, previous self-reported seizures (4.24, [2.69, 6.68], < 0.001) were most strongly associated with future self-reported seizures, and high perceived seizure risk (3.30, [1.97, 5.52], < 0.001) remained significant when prior self-reported seizures were added to the model. No significant association was found between e-survey responses and subsequent EEG seizures.

Significance

It appears that patients may tend to self-forecast seizures that occur in sequential groupings. Our results suggest that low mood and increased stress may be the result of previous seizures rather than independent premonitory symptoms. Patients in the small cohort with concurrent EEG showed no ability to self-predict EEG seizures. The conversion from OR to AUC values facilitates direct comparison of performance between survey and device studies involving survey premonition and forecasting.

Key points

Long-term e-surveys data and concurrent EEG signals were collected across three study sites to assess the ability of the patients to self-forecast their seizures.Patients may tend to self-forecast self-reported seizures that occur in sequential groupings.Factors, such as mood and stress, may not be independent premonitory symptoms but may be the consequence of recent seizures.No ability to self-forecast EEG confirmed seizures was observed in a small cohort with concurrent EEG validation.A mathematic relation between OR and AUC provides a means to compare forecasting performance between survey and device studies.

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.

Online Seizure Prediction System: A Novel Probabilistic Approach for Efficient Prediction of Epileptic Seizure with iEEG Signal

Background:

1% of people around the world are suffering from epilepsy. It is, therefore crucial to propose an efficient automated seizure prediction tool implemented in a portable device that uses the electroencephalogram (EEG) signal to enhance epileptic patients’ life quality.

Methods:

In this study, we focused on time-domain features to achieve discriminative information at a low CPU cost extracted from the intracranial electroencephalogram (iEEG) signals of six patients. The probabilistic framework based on XGBoost classifier requires the mean and maximum probability of the non-seizure and the seizure occurrence period segments. Once all these parameters are set for each patient, the medical decision maker can send alarm based on well-defined thresholds.

Results:

While finding a unique model for all patients is really challenging, and our modelling results demonstrated that the proposed algorithm can be an efficient tool for reliable and clinically relevant seizure forecasting. Using iEEG signals, the proposed algorithm can forecast seizures, informing a patient about 75 minutes before a seizure would occur, a period large enough for patients to take practical actions to minimize the potential impacts of the seizure.

Conclusion:

We posit that the ability to distinguish interictal intracranial EEG from pre-ictal signals at some low computational cost may be the first step towards an implanted portable semi-automatic seizure suppression system in the near future. It is believed that our seizure prediction technique can conceivably be coupled with treatment techniques aimed at interrupting the process even prior to a seizure initiates to develop.

The role of commercially available smartphone apps and wearable devices in monitoring patients after total knee arthroplasty: a systematic review

Purpose

Commercially available smartphone apps and wearable devices have proven valuable in a variety of clinical settings, yet their utility in measuring physical activity and monitoring patient status following total knee arthroplasty (TKA) remains unclear.

Methods

A systematic review was performed to assess the evidence supporting the use of smartphone apps and wearable devices to assist rehabilitation interventions following TKA. A search was conducted in the PubMed, Cochrane, Medline, and Web of Science databases in September 2021.

Results

One hundred and seventy-six studies were retrieved, of which 15 met inclusion criteria, including 6 randomized control trials. Four of these studies utilized smartphone apps, seven utilized wearable devices, and four utilized a combination of both. A total of 1607 TKA patients participated in the included studies. For primary outcomes, three reported on device accuracy, three on recovery prediction, two on functional recovery, two on physical activity promotion, two on patient compliance, two on pain control, and one on healthcare utilization.

Conclusion

Commercially available smartphone apps and wearable devices were shown to capably monitor physical activity and improve patient engagement following TKA, making them potentially viable adjuncts or replacements to traditional rehabilitation programs. Components of interventions such as step goals, app-based patient engagement platforms, and patient-specific benchmarks for recovery may improve effectiveness. However, future research should focus on the economics of implementation, long-term outcomes, and optimization of compliance and accuracy when using these devices.

Seizures detection using multimodal signals: a scoping review

INTRODUCTION

Epileptic seizures are common neurological disorders in the world, impacting 50 million people globally. Around 30% of patients with seizures suffer from refractory epilepsy, where seizures are not controlled by medications. The unpredictability of seizures makes it essential to have a continuous seizure monitoring system outside clinical settings for the purpose of minimizing patients' injuries and providing additional pathways for evaluation and treatment follow-up. Autonomic changes related to seizure events have been extensively studied and attempts made to apply them for seizure detection and prediction tasks. This scoping review aims to depict current research activities associated with the implementation of portable, wearable devices for seizure detection or prediction and inform future direction in continuous seizure tracking in ambulatory settings.

METHODS

Overall methodology framework includes 5 essential stages: research questions identification, relevant studies identification, selection of studies, data charting and summarizing the findings. A systematic searching strategy guided by systematic reviews and meta-analysis (PRISMA) was implemented to identify relevant records on two databases (PubMed, IEEE).

RESULTS

A total of 30 articles were included in our final analysis. Most of the studies were conducted off-line and employed consumer-graded wearable device. ACM is the dominant modality to be used in seizure detection, and widely deployed algorithms entail Support Vector Machine, Random Forest and threshold-based approach. The sensitivity ranged from 33.2% to 100% for single modality with a false alarm rate (FAR) ranging from 0.096 /day to 14.8 /day. Multimodality has a sensitivity ranging from 51% to 100% with FAR ranging from 0.12/day - 17.7/day.

CONCLUSION

The overall performance in seizure detection system based on non-cerebral physiological signals is promising, especially for the detection of motor seizures and seizures accompanied with intense ictal autonomic changes.

Modulation Spectral Signal Representation for Quality Measurement and Enhancement of Wearable Device Data: A Technical Note

Wearable devices are burgeoning, and applications across numerous verticals are emerging, including human performance monitoring, at-home patient monitoring, and health tracking, to name a few. Off-the-shelf wearables have been developed with focus on portability, usability, and low-cost. As such, when deployed in highly ecological settings, wearable data can be corrupted by artifacts and by missing data, thus severely hampering performance. In this technical note, we overview a signal processing representation called the modulation spectrum. The representation quantifies the rate-of-change of different spectral magnitude components and is shown to separate signal from noise, thus allowing for improved quality measurement, quality enhancement, and noise-robust feature extraction, as well as for disease characterization. We provide an overview of numerous applications developed by the authors over the last decade spanning different wearable modalities and list the results obtained from experimental results alongside comparisons with various state-of-the-art benchmark methods. Open-source software is showcased with the hope that new applications can be developed. We conclude with a discussion on possible future research directions, such as context awareness, signal compression, and improved input representations for deep learning algorithms.

Preferences and User Experiences of Wearable Devices in Epilepsy: A Systematic Review and Mixed-Methods Synthesis

BACKGROUND AND OBJECTIVES

To examine the preferences and user experiences of people with epilepsy and caregivers regarding automated wearable seizure detection devices.

METHODS

We performed a mixed-methods systematic review. We searched electronic databases for original peer-reviewed publications between January 1, 2000, and May 26, 2021. Key search terms included "epilepsy", "seizure", "wearable", and "non-invasive". We performed a descriptive and a qualitative thematic analysis of the studies included according to the technology acceptance model. Full texts of the discussion sections were further analyzed to identify word frequency and word mapping.

RESULTS

Twenty-two observational studies were identified. Collectively, they comprised responses from 3299 participants including patients with epilepsy, caregivers and healthcare workers. Sixteen studies examined user preferences, five examined user experiences, and one examined both experiences and preferences. Important preferences for wearables included improving care, cost, accuracy, and design. Patients desired real-time detection with a latency of ≤15 minutes from seizure occurrence, along with high sensitivity (≥90%) and low false-alarm rates. Device related costs were a major factor for device acceptance, where device costs of <$300 USD and a monthly subscription fee of <$20 USD were preferred. Despite being a major driver of wearable-based technologies, sudden unexpected death in epilepsy (SUDEP) was rarely discussed. Among studies evaluating user experiences, there was a greater acceptance towards wristwatches. Thematic coding analysis showed that attitudes towards device use, and perceived usefulness were reported consistently. Word mapping identified 'specificity', 'cost', and 'battery' as key single terms, and 'battery life', 'insurance coverage', 'prediction/detection quality', and the effect of devices on 'daily life' as key bigrams.

DISCUSSION

User acceptance of wearable technology for seizure detection was strongly influenced by accuracy, design, comfort, and cost. Our findings emphasise the need for standardised and validated tools to comprehensively examine preferences and user experiences of wearable devices in this population, using the themes identified in this study. Greater efforts to incorporate perspectives and user experiences in developing wearables for seizure detection, particularly in community-based settings are needed.

PROSPERO REGISTRATION

CRD42020193565.

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.

Medical Technology: A Systematic Review on Medical Devices Utilized for Epilepsy Prediction and Management

BACKGROUND

Epilepsy is a devastating neurological disorder that affects nearly 70 million people worldwide. Epilepsy causes uncontrollable, unprovoked and unpredictable seizures that reduce the quality of life of those afflicted, with 1-9 epileptic patient deaths per 1000 patients occurring annually due to sudden unexpected death in epilepsy (SUDEP). Predicting the onset of seizures and managing them may help patients from harming themselves and may improve their well-being. For a long time, electroencephalography (EEG) devices have been the mainstay for seizure detection and monitoring. This systematic review aimed to elucidate and critically evaluate the latest advancements in medical devices, besides EEG, that have been proposed for the management and prediction of epileptic seizures. A literature search was performed on three databases, PubMed, Scopus and EMBASE.

METHODS

Following title/abstract screening by two independent reviewers, 27 articles were selected for critical analysis in this review.

RESULTS

These articles revealed ambulatory, non-invasive and wearable medical devices, such as the in-ear EEG devices; the accelerometer-based devices and the subcutaneous implanted EEG devices might be more acceptable than traditional EEG systems. In addition, extracerebral signalbased devices may be more efficient than EEG-based systems, especially when combined with an intervention trigger. Although further studies may still be required to improve and validate these proposed systems before commercialization, these findings may give hope to epileptic patients, particularly those with refractory epilepsy, to predict and manage their seizures.

CONCLUSION

The use of medical devices for epilepsy may improve patients' independence and quality of life and possibly prevent sudden unexpected death in epilepsy (SUDEP).

Wearable device assessments of antiseizure medication effects on diurnal patterns of electrodermal activity, heart rate, and heart rate variability

Patient-generated health data provide a great opportunity for more detailed ambulatory monitoring and more personalized treatments in many diseases. In epilepsy, robust diagnostics applicable to the ambulatory setting are needed as diagnosis and treatment decisions in current clinical practice are primarily reliant on patient self-reports, which are often inaccurate. Recent work using wearable devices has focused on methods to detect and forecast epileptic seizures. Whether wearable device signals may also contain information about the effect of antiseizure medications (ASMs), which may ultimately help to better monitor their efficacy, has not been evaluated yet. Here we systematically investigated the effect of ASMs on different data modalities (electrodermal activity, EDA, heart rate, HR, and heart rate variability, HRV) simultaneously recorded by a wearable device in 48 patients with epilepsy over several days in the epilepsy long-term monitoring unit at a tertiary hospital. All signals exhibited characteristic diurnal variations. HRV, but not HR or EDA-based metrics, were reduced by ASMs. By assessing multiple signals related to the autonomic nervous system simultaneously, our results provide novel insights into the effects of ASMs on the sympathetic and parasympathetic interplay in the setting of epilepsy and indicate the potential of easy-to-wear wearable devices for monitoring ASM action. Future work using longer data may investigate these metrics on multidien cycles and their utility for detecting seizures, assessing seizure risk, or informing treatment interventions.

Interpretable EEG seizure prediction using a multiobjective evolutionary algorithm

Seizure prediction might be the solution to tackle the apparent unpredictability of seizures in patients with drug-resistant epilepsy, which comprise about a third of all patients with epilepsy. Designing seizure prediction models involves defining the pre-ictal period, a transition stage between inter-ictal brain activity and the seizure discharge. This period is typically a fixed interval, with some recent studies reporting the evaluation of different patient-specific pre-ictal intervals. Recently, researchers have aimed to determine the pre-ictal period, a transition stage between regular brain activity and a seizure. Authors have been using deep learning models given the ability of such models to automatically perform pre-processing, feature extraction, classification, and handling temporal and spatial dependencies. As these approaches create black-box models, clinicians may not have sufficient trust to use them in high-stake decisions. By considering these problems, we developed an evolutionary seizure prediction model that identifies the best set of features while automatically searching for the pre-ictal period and accounting for patient comfort. This methodology provides patient-specific interpretable insights, which might contribute to a better understanding of seizure generation processes and explain the algorithm’s decisions. We tested our methodology on 238 seizures and 3687 h of continuous data, recorded on scalp recordings from 93 patients with several types of focal and generalised epilepsies. We compared the results with a seizure surrogate predictor and obtained a performance above chance for 32% patients. We also compared our results with a control method based on the standard machine learning pipeline (pre-processing, feature extraction, classifier training, and post-processing), where the control marginally outperformed our approach by validating 35% of the patients. In total, 54 patients performed above chance for at least one method: our methodology or the control one. Of these 54 patients, 21 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\approx$$\end{document}≈38%) were solely validated by our methodology, while 24 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\approx$$\end{document}≈44%) were only validated by the control method. These findings may evidence the need for different methodologies concerning different patients.

Wearable Photoplethysmography for Cardiovascular Monitoring

Smart wearables provide an opportunity to monitor health in daily life and are emerging as potential tools for detecting cardiovascular disease (CVD). Wearables such as fitness bands and smartwatches routinely monitor the photoplethysmogram signal, an optical measure of the arterial pulse wave that is strongly influenced by the heart and blood vessels. In this survey, we summarize the fundamentals of wearable photoplethysmography and its analysis, identify its potential clinical applications, and outline pressing directions for future research in order to realize its full potential for tackling CVD.

Wearable electroencephalography for ultra-long-term seizure monitoring: a systematic review and future prospects

INTRODUCTION

: Wearable electroencephalography (EEG) for objective seizure counting might transform the clinical management of epilepsy. Non-EEG modalities have been validated for the detection of convulsive seizures, but there is still an unmet need for the detection of non-convulsive seizures.

AREAS COVERED

: The main objective of this systematic review was to explore the current status on wearable surface- and subcutaneous EEG for long-term seizure monitoring in epilepsy. We included 17 studies and evaluated the progress on the field, including device specifications, intended populations, and main results on the published studies including diagnostic accuracy measures. Furthermore, we examine the hurdles for widespread clinical implementation. This systematic review and expert opinion both consults the PRISMA guidelines and reflects on the future perspectives of this emerging field.

EXPERT OPINION

: Wearable EEG for long-term seizure monitoring is an emerging field, with plenty of proposed devices and proof-of-concept clinical validation studies. The possible implications of these devices are immense including objective seizure counting and possibly forecasting. However, the true clinical value of the devices, including effects on patient important outcomes and clinical decision making is yet to be unveiled and large-scale clinical validation trials are called for.

Ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning

The ability to forecast seizures minutes to hours in advance of an event has been verified using invasive EEG devices, but has not been previously demonstrated using noninvasive wearable devices over long durations in an ambulatory setting. In this study we developed a seizure forecasting system with a long short-term memory (LSTM) recurrent neural network (RNN) algorithm, using a noninvasive wrist-worn research-grade physiological sensor device, and tested the system in patients with epilepsy in the field, with concurrent invasive EEG confirmation of seizures via an implanted recording device. The system achieved forecasting performance significantly better than a random predictor for 5 of 6 patients studied, with mean AUC-ROC of 0.80 (range 0.72–0.92). These results provide the first clear evidence that direct seizure forecasts are possible using wearable devices in the ambulatory setting for many patients with epilepsy.

Autonomic manifestations of epilepsy: emerging pathways to sudden death?

Epileptic networks are intimately connected with the autonomic nervous system, as exemplified by a plethora of ictal (during a seizure) autonomic manifestations, including epigastric sensations, palpitations, goosebumps and syncope (fainting). Ictal autonomic changes might serve as diagnostic clues, provide targets for seizure detection and help us to understand the mechanisms that underlie sudden unexpected death in epilepsy (SUDEP). Autonomic alterations are generally more prominent in focal seizures originating from the temporal lobe, demonstrating the importance of limbic structures to the autonomic nervous system, and are particularly pronounced in focal-to-bilateral and generalized tonic-clonic seizures. The presence, type and severity of autonomic features are determined by the seizure onset zone, propagation pathways, lateralization and timing of the seizures, and the presence of interictal autonomic dysfunction. Evidence is mounting that not all autonomic manifestations are linked to SUDEP. In addition, experimental and clinical data emphasize the heterogeneity of SUDEP and its infrequent overlap with sudden cardiac death. Here, we review the spectrum and diagnostic value of the mostly benign and self-limiting autonomic manifestations of epilepsy. In particular, we focus on presentations that are likely to contribute to SUDEP and discuss how wearable devices might help to prevent SUDEP.

The Challenging Path to Developing a Mobile Health Device for Epilepsy: The Current Landscape and Where We Go From Here

Seizure detection, and more recently seizure forecasting, represent important avenues of clinical development in epilepsy, promoted by progress in wearable devices and mobile health (mHealth), which might help optimizing seizure control and prevention of seizure-related mortality and morbidity in persons with epilepsy. Yet, very long-term continuous monitoring of seizure-sensitive biosignals in the ambulatory setting presents a number of challenges. We herein provide an overview of these challenges and current technological landscape of mHealth devices for seizure detection. Specifically, we display, which types of sensor modalities and analytical methods are available, and give insight into current clinical practice guidelines, main outcomes of clinical validation studies, and discuss how to evaluate device performance at point-of-care facilities. We then address pitfalls which may arise in patient compliance and the need to design solutions adapted to user experience.

Identification of Ictal Tachycardia in Focal Motor- and Non-Motor Seizures by Means of a Wearable PPG Sensor

Photoplethysmography (PPG) as an additional biosignal for a seizure detector has been underutilized so far, which is possibly due to its susceptibility to motion artifacts. We investigated 62 focal seizures from 28 patients with electrocardiography-based evidence of ictal tachycardia (IT). Seizures were divided into subgroups: those without epileptic movements and those with epileptic movements not affecting and affecting the extremities. PPG-based heart rate (HR) derived from a wrist-worn device was calculated for sections with high signal quality, which were identified using spectral entropy. Overall, IT based on PPG was identified in 37 of 62 (60%) seizures (9/19, 7/8, and 21/35 in the three groups, respectively) and could be found prior to the onset of epileptic movements affecting the extremities in 14/21 seizures. In 30/37 seizures, PPG-based IT was in good temporal agreement (<10 s) with ECG-based IT, with an average delay of 5.0 s relative to EEG onset. In summary, we observed that the identification of IT by means of a wearable PPG sensor is possible not only for non-motor seizures but also in motor seizures, which is due to the early manifestation of IT in a relevant subset of focal seizures. However, both spontaneous and epileptic movements can impair PPG-based seizure detection.

Prospective Study of a Multimodal Convulsive Seizure Detection Wearable System on Pediatric and Adult Patients in the Epilepsy Monitoring Unit

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.

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.

Epilepsy Personal Assistant Device-A Mobile Platform for Brain State, Dense Behavioral and Physiology Tracking and Controlling Adaptive Stimulation

Epilepsy is one of the most common neurological disorders, and it affects almost 1% of the population worldwide. Many people living with epilepsy continue to have seizures despite anti-epileptic medication therapy, surgical treatments, and neuromodulation therapy. The unpredictability of seizures is one of the most disabling aspects of epilepsy. Furthermore, epilepsy is associated with sleep, cognitive, and psychiatric comorbidities, which significantly impact the quality of life. Seizure predictions could potentially be used to adjust neuromodulation therapy to prevent the onset of a seizure and empower patients to avoid sensitive activities during high-risk periods. Long-term objective data is needed to provide a clearer view of brain electrical activity and an objective measure of the efficacy of therapeutic measures for optimal epilepsy care. While neuromodulation devices offer the potential for acquiring long-term data, available devices provide very little information regarding brain activity and therapy effectiveness. Also, seizure diaries kept by patients or caregivers are subjective and have been shown to be unreliable, in particular for patients with memory-impairing seizures. This paper describes the design, architecture, and development of the Mayo Epilepsy Personal Assistant Device (EPAD). The EPAD has bi-directional connectivity to the implanted investigational Medtronic Summit RC+STM device to implement intracranial EEG and physiological monitoring, processing, and control of the overall system and wearable devices streaming physiological time-series signals. In order to mitigate risk and comply with regulatory requirements, we developed a Quality Management System (QMS) to define the development process of the EPAD system, including Risk Analysis, Verification, Validation, and protocol mitigations. Extensive verification and validation testing were performed on thirteen canines and benchtop systems. The system is now under a first-in-human trial as part of the US FDA Investigational Device Exemption given in 2018 to study modulated responsive and predictive stimulation using the Mayo EPAD system and investigational Medtronic Summit RC+STM in ten patients with non-resectable dominant or bilateral mesial temporal lobe epilepsy. The EPAD system coupled with an implanted device capable of EEG telemetry represents a next-generation solution to optimizing neuromodulation therapy.

Cycles in epilepsy

Epilepsy is among the most dynamic disorders in neurology. A canonical view holds that seizures, the characteristic sign of epilepsy, occur at random, but, for centuries, humans have looked for patterns of temporal organization in seizure occurrence. Observations that seizures are cyclical date back to antiquity, but recent technological advances have, for the first time, enabled cycles of seizure occurrence to be quantitatively characterized with direct brain recordings. Chronic recordings of brain activity in humans and in animals have yielded converging evidence for the existence of cycles of epileptic brain activity that operate over diverse timescales: daily (circadian), multi-day (multidien) and yearly (circannual). Here, we review this evidence, synthesizing data from historical observational studies, modern implanted devices, electronic seizure diaries and laboratory-based animal neurophysiology. We discuss advances in our understanding of the mechanistic underpinnings of these cycles and highlight the knowledge gaps that remain. The potential clinical applications of a knowledge of cycles in epilepsy, including seizure forecasting and chronotherapy, are discussed in the context of the emerging concept of seizure risk. In essence, this Review addresses the broad question of why seizures occur when they occur.

Non-invasive wearable seizure detection using long-short-term memory networks with transfer learning

Objective. The detection of seizures using wearable devices would improve epilepsy management, but reliable detection of seizures in an ambulatory environment remains challenging, and current studies lack concurrent validation of seizures using electroencephalography (EEG) data.Approach. An adaptively trained long-short-term memory deep neural network was developed and trained using a modest number of seizure data sets from wrist-worn devices. Transfer learning was used to adapt a classifier that was initially trained on intracranial electroencephalography (iEEG) signals to facilitate classification of non-EEG physiological datasets comprising accelerometry, blood volume pulse, skin electrodermal activity, heart rate, and temperature signals. The algorithm's performance was assessed with and without pre-training on iEEG signals and transfer learning. To assess the performance of the seizure detection classifier using long-term ambulatory data, wearable devices were used for multiple months with an implanted neurostimulator capable of recording iEEG signals, which provided independent electrographic seizure detections that were reviewed by a board-certified epileptologist.Main results. For 19 motor seizures from 10 in-hospital patients, the algorithm yielded a mean area under curve (AUC), a sensitivity, and an false alarm rate per day (FAR/day) of 0.98, 0.93, and 2.3, respectively. Additionally, for eight seizures with probable motor semiology from two ambulatory patients, the classifier achieved a mean AUC of 0.97 and an FAR of 2.45 events/day at a sensitivity of 0.9. For all seizure types in the ambulatory setting, the classifier had a mean AUC of 0.82 with a sensitivity of 0.47 and an FAR of 7.2 events/day.Significance. The performance of the algorithm was evaluated using motor and non-motor seizures during in-hospital and ambulatory use. The classifier was able to detect multiple types of motor and non-motor seizures, but performed significantly better on motor seizures.

Remote Monitoring of Critically-Ill Post-Surgical Patients: Lessons from a Biosensor Implementation Trial

Biosensors represent one of the numerous promising technologies envisioned to extend healthcare delivery. In perioperative care, the healthcare delivery system can use biosensors to remotely supervise patients who would otherwise be admitted to a hospital. This novel technology has gained a foothold in healthcare with significant acceleration due to the COVID-19 pandemic. However, few studies have attempted to narrate, or systematically analyze, the process of their implementation. We performed an observational study of biosensor implementation. The data accuracy provided by the commercially available biosensors was compared to those offered by standard clinical monitoring on patients admitted to the intensive care unit/perioperative unit. Surveys were also conducted to examine the acceptance of technology by patients and medical staff. We demonstrated a significant difference in vital signs between sensors and standard monitoring which was very dependent on the measured variables. Sensors seemed to integrate into the workflow relatively quickly, with almost no reported problems. The acceptance of the biosensors was high by patients and slightly less by nurses directly involved in the patients’ care. The staff forecast a broad implementation of biosensors in approximately three to five years, yet are eager to learn more about them. Reliability considerations proved particularly troublesome in our implementation trial. Careful evaluation of sensor readiness is most likely necessary prior to system-wide implementation by each hospital to assess for data accuracy and acceptance by the staff.

Automated seizure detection using wearable devices: A clinical practice guideline of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology

The objective of this clinical practice guideline (CPG) is to provide recommendations for healthcare personnel working with patients with epilepsy, on the use of wearable devices for automated seizure detection in patients with epilepsy, in outpatient, ambulatory settings. The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology developed the CPG according to the methodology proposed by the ILAE Epilepsy Guidelines Working Group. We reviewed the published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement and evaluated the evidence and formulated the recommendations following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. We found high level of evidence for the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) and focal-to-bilateral tonic-clonic seizures (FBTCS) and recommend use of wearable automated seizure detection devices for selected patients when accurate detection of GTCS and FBTCS is recommended as a clinical adjunct. We also found moderate level of evidence for seizure types without GTCs or FBTCs. However, it was uncertain whether the detected alarms resulted in meaningful clinical outcomes for the patients. We recommend using clinically validated devices for automated detection of GTCS and FBTCS, especially in unsupervised patients, where alarms can result in rapid intervention (weak/conditional recommendation). At present, we do not recommend clinical use of the currently available devices for other seizure types (weak/conditional recommendation). Further research and development are needed to improve the performance of automated seizure detection and to document their accuracy and clinical utility.

Wearables bei Epilepsien

Epileptische Anfälle führen zu verschiedensten körperlichen Symptomen, die je nach Art und Ausprägung mit geeigneten Geräten gemessen werden und als Surrogatmarker epileptischer Anfälle dienen können. Dominierende motorische Symptome können mit Beschleunigungssensoren oder elektromyografisch erfasst werden. Bei fokalen Anfällen mit fehlender oder geringer motorischer Beteiligung können autonome Phänomene wie Änderungen der Herzrate, Atmung und des elektrischen Hautwiderstandes per Elektrokardiografie, Photopletysmografie und Hautsensoren gemessen werden. Die in den heutigen Wearables integrierten Sensoren können diese Körpersignale messen und zur automatisierten Anfallserkennung nutzbar machen. In dieser Übersichtsarbeit werden verschiedene Sensortechnologien, Wearables und deren Anwendung zur automatisierten Erkennung epileptischer Anfälle vorgestellt, Gütekriterien zur Einschätzung mobiler Gesundheitstechnologien diskutiert und klinisch geprüfte Systeme zusammengefasst.

Implementing Remote Memory Clinics to Enhance Clinical Care During and After COVID-19

Social isolation is likely to be recommended for older adults due to COVID-19, with ongoing reduced clinical contact suggested for this population. This has increased the need for remote memory clinics, we therefore review the literature, current practices and guidelines on organizing such remote memory clinics, focusing on assessment of cognition, function and other relevant measurements, proposing a novel pathway based on three levels of complexity: simple telephone or video-based interviews and testing using available tests (Level 1), digitized and validated methods based on standard pen-and-paper tests and scales (Level 2), and finally fully digitized cognitive batteries and remote measurement technologies (RMTs, Level 3). Pros and cons of these strategies are discussed. Remotely collected data negates the need for frail patients or carers to commute to clinic and offers valuable insights into progression over time, as well as treatment responses to therapeutic interventions, providing a more realistic and contextualized environment for data-collection. Notwithstanding several challenges related to internet access, computer skills, limited evidence base and regulatory and data protection issues, digital biomarkers collected remotely have significant potential for diagnosis and symptom management in older adults and we propose a framework and pathway for how technologies can be implemented to support remote memory clinics. These platforms are also well-placed for administration of digital cognitive training and other interventions. The individual, societal and public/private costs of COVID-19 are high and will continue to rise for some time but the challenges the pandemic has placed on memory services also provides an opportunity to embrace novel approaches. Remote memory clinics' financial, logistical, clinical and practical benefits have been highlighted by COVID-19, supporting their use to not only be maintained when social distancing legislation is lifted but to be devoted extra resources and attention to fully potentiate this valuable arm of clinical assessment and care.