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

New wearable and portable EEG modalities in epilepsy: The views of hospital-based healthcare professionals

BACKGROUND

Novel mobile and portable EEG solutions, designed for short and long-term monitoring of individuals with epilepsy have been developed in recent years but, they are underutilized, lacking full integration into clinical routine. Exploring the opinions of hospital-based healthcare professionals regarding their potential application, technical requirements and value would be crucial for future device development and increase their clinical application.

PURPOSE

To evaluate professionals' opinions on novel EEG systems, focusing on their potential application in various clinical settings, professionals' interest in non-invasive solutions for ultra-long monitoring of people with epilepsy (PWE) and factors which could increase future use of novel EEG systems.

MATERIALS AND METHODS

We conducted an online survey where Hospital-based professionals shared opinions on potential advantages, clinical value, and key features of novel wearable EEG systems in five different clinical settings. Additionally, insights were gathered on the need for future research and, the need for additional information about devices from companies and researchers.

RESULTS

Respondents (n = 40) prioritized high performance, data quality, easy patient mobility, and comfort as crucial features for novel devices. Advantages were highlighted, including more natural settings, reduced application time, earlier epilepsy diagnosis, and decreased support requirements. Novel EEG devices were seen as valuable for epilepsy diagnosis, seizure monitoring, automatic seizure documentation, seizure alarms, and seizure forecasting. Interest in integrating these new systems into clinical practice was high, particularly for supervising drug-resistant epilepsy, reducing SUDEP, and detecting nocturnal seizures. Professionals emphasized the need for more research studies and highlighted the need for increased information from companies and researchers.

CONCLUSIONS

Professionals underscore specific technical and practical features, along with potential clinical advantages and value of novel EEG devices that could drive their development. While interest in integrating these solutions in clinical practice exists, further validation studies and enhanced communication between researchers, companies, and clinicians are crucial for overcoming potential scepticism and facilitating widespread adoption.

Resting-state frontal electroencephalography (EEG) biomarkers for detecting the severity of chronic neuropathic pain

Increasing evidence is present to enable pain measurement by using frontal channel EEG-based signals with spectral analysis and phase-amplitude coupling. To identify frontal channel EEG-based biomarkers for quantifying pain severity, we investigated band-power features to more complex features and employed various machine learning algorithms to assess the viability of these features. We utilized a public EEG dataset obtained from 36 patients with chronic pain during an eyes-open resting state and performed correlation analysis between clinically labelled pain scores and EEG features from Fp1 and Fp2 channels (EEG band-powers, phase-amplitude couplings (PAC), and its asymmetry features). We also conducted regression analysis with various machine learning models to predict patients' pain intensity. All the possible feature sets combined with five machine learning models (Linear Regression, random forest and support vector regression with linear, non-linear and polynomial kernels) were intensively checked, and regression performances were measured by adjusted R-squared value. We found significant correlations between beta power asymmetry (r = -0.375), gamma power asymmetry (r = -0.433) and low beta to low gamma coupling (r = -0.397) with pain scores while band power features did not show meaningful results. In the regression analysis, Support Vector Regression with a polynomial kernel showed the best performance (R squared value = 0.655), enabling the regression of pain intensity within a clinically usable error range. We identified the four most selected features (gamma power asymmetry, PAC asymmetry of theta to low gamma, low beta to low/high gamma). This study addressed the importance of complex features such as asymmetry and phase-amplitude coupling in pain research and demonstrated the feasibility of objectively observing pain intensity using the frontal channel-based EEG, that are clinically crucial for early intervention.

Economic aspects of prolonged home video-EEG monitoring: a simulation study

INTRODUCTION

Video EEG monitoring (VEM) is an important tool for characterizing clinical events suspected as seizures. It is also used for pre-surgical workups in patients with drug-resistant epilepsy (DRE). In-hospital VEM high cost, long admission waiting periods and some other inconveniences led to an interest in home VEM (HVEM). However, because antiseizure medications cannot be reduced at home, HVEM may require longer monitoring. While the economic aspect is one of the main motivations for HVEM, the cost of HVEM lasting several weeks has not been assessed.

METHODS

We modeled the cost of HVEM for 8 weeks and compared it to the cost of 1-week in-hospital VEM. Additionally, we modeled the per-patient cost for a combination of HVEM and in-hospital VEM, considering that if in a proportion of patients HVEM fails to achieve its goal, they should undergo in-hospital VEM with drug reduction.

RESULTS

The average cost of HVEM up to 4-6 weeks of monitoring was lower than that for the 1-week in-hospital VEM. Combining the 3-week HVEM with 1-week in-hospital VEM (if needed) reduced the per-patient cost by 6.6-28.6% as compared to the situation when all the patients with DRE were referred to the in-hospital VEM.

CONCLUSIONS

A prolonged intermittent HVEM can be cost-effective, especially if the minimal seizure frequency is about one seizure per week. The study findings support directing efforts into clinical trials and technology development.

Towards predicting PTSD symptom severity using portable EEG-derived biomarkers

Posttraumatic Stress Disorder (PTSD) is a heterogeneous mental health disorder that occurs following traumatic experience. Understanding its neurobiological basis is crucial to advance early diagnosis and treatment. Electroencephalography (EEG) can be used to explore the neurobiological basis of PTSD. However, only limited research has explored mobile EEG, which is important for scalability. This proof-of-concept study delves into mobile EEG-derived biomarkers for PTSD and their potential implications. Over four weeks, we measured PTSD symptoms using the PTSD checklist for DSM-5 (PCL-5) at multiple timepoints, and we recorded multiple EEG sessions from 21 individuals using a mobile EEG device. In total, we captured 38 EEG sessions, each comprising two recordings that lasted approximately 180 seconds, to evaluate reproducibility. Next, we extracted Shannon entropy, as a measure of the randomness or unpredictability of the signal and spectral power for the fronto-temporal regions of interest, including electrodes at AF3, AF4, T7, and T8 for each EEG recording session. We calculated the partial correlation between the EEG variables and PCL-5 measured closest to the EEG session, using age, sex, and the grouping variable 'batch' as covariates. We observed a significant negative correlation between Shannon entropy in fronto-temporal regions and PCL-5 scores. Specifically, this association was evident in the AF3 ( r = -0.456, FDR-corrected p = 0.01), AF4 ( r = -0.362, FDR-corrected p = 0.04), and T7 ( r = -0.472, FDR-corrected p = 0.01) regions. Additionally, we found a significant negative association between the alpha power estimated from AF4 and PCL-5 ( r =-0.429, FDR-corrected p =0.04). Our findings suggest that EEG data acquired using a mobile EEG device is associated with PTSD symptom severity, offering valuable insights into the neurobiological mechanisms underlying PTSD.

EEG Changes in Migraine-Can EEG Help to Monitor Attack Susceptibility?

Migraine is a highly prevalent brain condition with paroxysmal changes in brain excitability believed to contribute to the initiation of an attack. The attacks and their unpredictability have a major impact on the lives of patients. Clinical management is hampered by a lack of reliable predictors for upcoming attacks, which may help in understanding pathophysiological mechanisms to identify new treatment targets that may be positioned between the acute and preventive possibilities that are currently available. So far, a large range of studies using conventional hospital-based EEG recordings have provided contradictory results, with indications of both cortical hyper- as well as hypo-excitability. These heterogeneous findings may largely be because most studies were cross-sectional in design, providing only a snapshot in time of a patient's brain state without capturing day-to-day fluctuations. The scope of this narrative review is to (i) reflect on current knowledge on EEG changes in the context of migraine, the attack cycle, and underlying pathophysiology; (ii) consider the effects of migraine treatment on EEG features; (iii) outline challenges and opportunities in using EEG for monitoring attack susceptibility; and (iv) discuss future applications of EEG in home-based settings.

Challenges and Prospects in Epilepsy Monitoring Units: A Comprehensive Review of Logistic Barriers

Epilepsy is one of the most common neurological diseases with a prevalence ranging from 0.5% to 2% in different sittings. The World Health Organization (WHO) estimated that nearly 80% of this burden is borne by resource-poor countries where even conventional electroencephalogram (EEG) coverage is dramatically short.  Video EEG monitoring applied for days as conducted in epilepsy monitoring units (EMUs) is aimed at seizure localization, anti-seizure medication (ASM) adjustment, or epilepsy surgery evaluation and planning. However, the EEG approach in EMUs has its obstacles. The present article is aimed to concentrate on the logistic challenges of EMUs, discussing existing data and limitations and offering suggestions for future planning to enhance the utilization of existing technology. Shortages of adult and pediatric epileptologists, qualified nurses, as well as EEG technologists have been reported in different countries. Moreover, injuries and falls, psychosis, status epilepticus, and unexpected death have been stated to be the most frequent safety issues in EMUs. Enhancements to mitigate logistical and healthcare system-related barriers in EMUs include the implementation of large cohort studies and the utilization of artificial intelligence (AI) for the identification and categorization of specific risks among EMU admissions. The establishment of EMUs and their associated challenges and barriers are best acknowledged through discussions and dialogue with various stakeholders.

Real-Time Seizure Detection Using Behind-the-Ear Wearable System

INTRODUCTION

This study examines the usability and comfort of a behind-the-ear seizure detection device called brain seizure detection (BrainSD) that captures ictal electroencephalogram (EEG) data using four scalp electrodes.

METHODS

This is a feasibility study. Thirty-two patients admitted to a level 4 Epilepsy Monitoring Unit were enrolled. The subjects wore BrainSD and the standard 21-channel video-EEG simultaneously. Epileptologists analyzed the EEG signals collected by BrainSD and validated it using video-EEG data to confirm its accuracy. A poststudy survey was completed by each participant to evaluate the comfort and usability of the device. In addition, a focus group of UT Southwestern epileptologists was held to discuss the features they would like to see in a home EEG-based seizure detection device such as BrainSD.

RESULTS

In total, BrainSD captured 11 of the 14 seizures that occurred while the device was being worn. All 11 seizures captured on BrainSD had focal onset, with three becoming bilateral tonic-clonic and one seizure being of subclinical status. The device was worn for an average of 41 hours. The poststudy survey showed that most users found the device comfortable, easy-to-use, and stated they would be interested in using BrainSD. Epileptologists in the focus group expressed a similar interest in BrainSD.

CONCLUSIONS

Brain seizure detection is able to detect EEG signals using four behind-the-ear electrodes. Its comfort, ease-of-use, and ability to detect numerous types of seizures make BrainSD an acceptable at-home EEG detection device from both the patient and provider perspective.

Home recording of 3-Hz spike-wave discharges in adults with absence epilepsy using the wearable Sensor Dot

OBJECTIVE

Home monitoring of 3-Hz spike-wave discharges (SWDs) in patients with refractory absence epilepsy could improve clinical care by replacing the inaccurate seizure diary with objective counts. We investigated the use and performance of the Sensor Dot (Byteflies) wearable in persons with absence epilepsy in their home environment.

METHODS

Thirteen participants (median age = 22 years, 11 female) were enrolled at the university hospitals of Leuven and Freiburg. At home, participants had to attach the Sensor Dot and behind-the-ear electrodes to record two-channel electroencephalogram (EEG), accelerometry, and gyroscope data. Ground truth annotations were created during a visual review of the full Sensor Dot recording. Generalized SWDs were annotated if they were 3 Hz and at least 3 s on EEG. Potential 3-Hz SWDs were flagged by an automated seizure detection algorithm, (1) using only EEG and (2) with an additional postprocessing step using accelerometer and gyroscope to discard motion artifacts. Afterward, two readers (W.V.P. and L.S.) reviewed algorithm-labeled segments and annotated true positive detections. Sensitivity, precision, and F1 score were calculated. Patients had to keep a seizure diary and complete questionnaires about their experiences.

RESULTS

Total recording time was 394 h 42 min. Overall, 234 SWDs were captured in 11 of 13 participants. Review of the unimodal algorithm-labeled recordings resulted in a mean sensitivity of .84, precision of .93, and F1 score of .89. Visual review of the multimodal algorithm-labeled segments resulted in a similar F1 score and shorter review time due to fewer false positive labels. Participants reported that the device was comfortable and that they would be willing to wear it on demand of their neurologist, for a maximum of 1 week or with intermediate breaks.

SIGNIFICANCE

The Sensor Dot improved seizure documentation at home, relative to patient self-reporting. Additional benefits were the short review time and the patients' device acceptance due to user-friendliness and comfortability.

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.

Economic review of point-of-care EEG

Aims: Point-of-care electroencephalogram (POC-EEG) is an acute care bedside screening tool for the identification of nonconvulsive seizures (NCS) and nonconvulsive status epilepticus (NCSE). The objective of this narrative review is to describe the economic themes related to POC-EEG in the United States (US).Materials and methods: We examined peer-reviewed, published manuscripts on the economic findings of POC-EEG for bedside use in US hospitals, which included those found through targeted searches on PubMed and Google Scholar. Conference abstracts, gray literature offerings, frank advertisements, white papers, and studies conducted outside the US were excluded.Results: Twelve manuscripts were identified and reviewed; results were then grouped into four categories of economic evidence. First, POC-EEG usage was associated with clinical management amendments and antiseizure medication reductions. Second, POC-EEG was correlated with fewer unnecessary transfers to other facilities for monitoring and reduced hospital length of stay (LOS). Third, when identifying NCS or NCSE onsite, POC-EEG was associated with greater reimbursement in Medical Severity-Diagnosis Related Group coding. Fourth, POC-EEG may lower labor costs via decreasing after-hours requests to EEG technologists for conventional EEG (convEEG).Limitations: We conducted a narrative review, not a systematic review. The studies were observational and utilized one rapid circumferential headband system, which limited generalizability of the findings and indicated publication bias. Some sample sizes were small and hospital characteristics may not represent all US hospitals. POC-EEG studies in pediatric populations were also lacking. Ultimately, further research is justified.Conclusions: POC-EEG is a rapid screening tool for NCS and NCSE in critical care and emergency medicine with potential financial benefits through refining clinical management, reducing unnecessary patient transfers and hospital LOS, improving reimbursement, and mitigating burdens on healthcare staff and hospitals. Since POC-EEG has limitations (i.e. no video component and reduced montage), the studies asserted that it did not replace convEEG.

Using Explainable Artificial Intelligence to Obtain Efficient Seizure-Detection Models Based on Electroencephalography Signals

Epilepsy is a condition that affects 50 million individuals globally, significantly impacting their quality of life. Epileptic seizures, a transient occurrence, are characterized by a spectrum of manifestations, including alterations in motor function and consciousness. These events impose restrictions on the daily lives of those affected, frequently resulting in social isolation and psychological distress. In response, numerous efforts have been directed towards the detection and prevention of epileptic seizures through EEG signal analysis, employing machine learning and deep learning methodologies. This study presents a methodology that reduces the number of features and channels required by simpler classifiers, leveraging Explainable Artificial Intelligence (XAI) for the detection of epileptic seizures. The proposed approach achieves performance metrics exceeding 95% in accuracy, precision, recall, and F1-score by utilizing merely six features and five channels in a temporal domain analysis, with a time window of 1 s. The model demonstrates robust generalization across the patient cohort included in the database, suggesting that feature reduction in simpler models-without resorting to deep learning-is adequate for seizure detection. The research underscores the potential for substantial reductions in the number of attributes and channels, advocating for the training of models with strategically selected electrodes, and thereby supporting the development of effective mobile applications for epileptic seizure detection.

Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data

Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these issues by transferring knowledge from a previously trained task to develop high-performance prediction models for a new task. This survey paper provides a comprehensive study of the effectiveness of transfer learning for digital health applications to enhance the accuracy and efficiency of diagnoses and prognoses, as well as to improve healthcare services. The first part of this survey paper presents and discusses the most common digital health sensing technologies as valuable data resources for machine learning applications, including transfer learning. The second part discusses the meaning of transfer learning, clarifying the categories and types of knowledge transfer. It also explains transfer learning methods and strategies, and their role in addressing the challenges in developing accurate machine learning models, specifically on digital health sensing data. These methods include feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning. This survey paper highlights the key features of each transfer learning method and strategy, and discusses the limitations and challenges of using transfer learning for digital health applications. Overall, this paper is a comprehensive survey of transfer learning methods on digital health sensing data which aims to inspire researchers to gain knowledge of transfer learning approaches and their applications in digital health, enhance the current transfer learning approaches in digital health, develop new transfer learning strategies to overcome the current limitations, and apply them to a variety of digital health technologies.

At home EEG monitoring technologies for people with epilepsy and intellectual disabilities: A scoping review

BACKGROUND

Conducting electroencephalography in people with intellectual disabilities (PwID) can be challenging, but the high proportion of PwID who experience seizures make it an essential part of their care. To reduce hospital-based monitoring, interventions are being developed to enable high-quality EEG data to be collected at home. This scoping review aims to summarise the current state of remote EEG monitoring research, potential benefits and limitations of the interventions, and inclusion of PwID in this research.

METHODS

The review was structured using the PRISMA extension for Scoping Reviews and the PICOS framework. Studies that evaluated a remote EEG monitoring intervention in adults with epilepsy were retrieved from the PubMed, MEDLINE, Embase, CINAHL, Web of Science, and ClinicalTrials.gov databases. A descriptive analysis provided an overview of the study and intervention characteristics, key results, strengths, and limitations.

RESULTS

34,127 studies were retrieved and 23 were included. Five types of remote EEG monitoring were identified. Common benefits included producing useful results of comparable quality to inpatient monitoring and patient experience. A common limitation was the challenge of capturing all seizures with a small number of localised electrodes. No randomised controlled trials were included, few studies reported sensitivity and specificity, and only three considered PwID.

CONCLUSIONS

Overall, the studies demonstrated the feasibility of remote EEG interventions for out-of-hospital monitoring and their potential to improve data collection and quality of care for patients. Further research is needed on the effectiveness, benefits, and limitations of remote EEG monitoring compared to in-patient monitoring, especially for PwID.

Diversity and Suitability of the State-of-the-Art Wearable and Wireless EEG Systems Review

Wireless electroencephalography (EEG) systems have been attracting increasing attention in recent times. Both the number of articles discussing wireless EEG and their proportion relative to general EEG publications have increased over years. These trends indicate that wireless EEG systems could be more accessible to researchers and the research community has recognized the potential of wireless EEG systems. To explore the development and diverse applications of wireless EEG systems, this review highlights the trends in wearable and wireless EEG systems over the past decade and compares the specifications and research applications of the major wireless systems marketed by 16 companies. For each product, five parameters (number of channels, sampling rate, cost, battery life, and resolution) were assessed for comparison. Currently, these wearable and portable wireless EEG systems have three main application areas: consumer, clinical, and research. To address this multitude of options, the article also discussed the thought process to find a suitable device that meets personalization and use cases specificities. These investigations suggest that low-price and convenience are key factors for consumer applications, wireless EEG systems with FDA or CE-certification may be more suitable for clinical settings, and devices that provide raw EEG data with high-density channels are important for laboratory research. This article presents an overview of the current state of the wireless EEG systems specifications and possible applications and serves as a guide point as it is expected that more influential and novel research will cyclically promote the development of such EEG systems.

Seizure detection with reduced electroencephalogram channels: research trends and outlook

Epilepsy is a prevalent condition characterized by recurrent, unpredictable seizures. Monitoring with surface electroencephalography (EEG) is the gold standard for diagnosing epilepsy, but a time-consuming, uncomfortable and sometimes ineffective process for patients. Further, using EEG over a brief monitoring period has variable success, dependent on patient tolerance and seizure frequency. The availability of hospital resources and hardware and software specifications inherently restrict the options for comfortable, long-term data collection, resulting in limited data for training machine-learning models. This mini-review examines the current patient journey, providing an overview of the current state of EEG monitoring with reduced electrodes and automated channel reduction methods. Opportunities for improving data reliability through multi-modal data fusion are suggested. We assert the need for further research in electrode reduction to advance brain monitoring solutions towards portable, reliable devices that simultaneously offer patient comfort, perform ultra-long-term monitoring and expedite the diagnosis process.

Assessment of a 16-Channel Ambulatory Dry Electrode EEG for Remote Monitoring

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

A Review of Skin-Wearable Sensors for Non-Invasive Health Monitoring Applications

The early detection of fatal diseases is crucial for medical diagnostics and treatment, both of which benefit the individual and society. Portable devices, such as thermometers and blood pressure monitors, and large instruments, such as computed tomography (CT) and X-ray scanners, have already been implemented to collect health-related information. However, collecting health information using conventional medical equipment at home or in a hospital can be inefficient and can potentially affect the timeliness of treatment. Therefore, on-time vital signal collection via healthcare monitoring has received increasing attention. As the largest organ of the human body, skin delivers significant signals reflecting our health condition; thus, receiving vital signals directly from the skin offers the opportunity for accessible and versatile non-invasive monitoring. In particular, emerging flexible and stretchable electronics demonstrate the capability of skin-like devices for on-time and continuous long-term health monitoring. Compared to traditional electronic devices, this type of device has better mechanical properties, such as skin conformal attachment, and maintains compatible detectability. This review divides the health information that can be obtained from skin using the sensor aspect's input energy forms into five categories: thermoelectrical signals, neural electrical signals, photoelectrical signals, electrochemical signals, and mechanical pressure signals. We then summarize current skin-wearable health monitoring devices and provide outlooks on future development.

Unsupervised EEG preictal interval identification in patients with drug-resistant epilepsy

Typical seizure prediction models aim at discriminating interictal brain activity from pre-seizure electrographic patterns. Given the lack of a preictal clinical definition, a fixed interval is widely used to develop these models. Recent studies reporting preictal interval selection among a range of fixed intervals show inter- and intra-patient preictal interval variability, possibly reflecting the heterogeneity of the seizure generation process. Obtaining accurate labels of the preictal interval can be used to train supervised prediction models and, hence, avoid setting a fixed preictal interval for all seizures within the same patient. Unsupervised learning methods hold great promise for exploring preictal alterations on a seizure-specific scale. Multivariate and univariate linear and nonlinear features were extracted from scalp electroencephalography (EEG) signals collected from 41 patients with drug-resistant epilepsy undergoing presurgical monitoring. Nonlinear dimensionality reduction was performed for each group of features and each of the 226 seizures. We applied different clustering methods in searching for preictal clusters located until 2 h before the seizure onset. We identified preictal patterns in 90% of patients and 51% of the visually inspected seizures. The preictal clusters manifested a seizure-specific profile with varying duration (22.9 ± 21.0 min) and starting time before seizure onset (47.6 ± 27.3 min). Searching for preictal patterns on the EEG trace using unsupervised methods showed that it is possible to identify seizure-specific preictal signatures for some patients and some seizures within the same patient.

Novel subscalp and intracranial devices to wirelessly record and analyze continuous EEG in unsedated, behaving dogs in their natural environments: A new paradigm in canine epilepsy research

Epilepsy is characterized by unprovoked, recurrent seizures and is a common neurologic disorder in dogs and humans. Roughly 1/3 of canines and humans with epilepsy prove to be drug-resistant and continue to have sporadic seizures despite taking daily anti-seizure medications. The optimization of pharmacologic therapy is often limited by inaccurate seizure diaries and medication side effects. Electroencephalography (EEG) has long been a cornerstone of diagnosis and classification in human epilepsy, but because of several technical challenges has played a smaller clinical role in canine epilepsy. The interictal (between seizures) and ictal (seizure) EEG recorded from the epileptic mammalian brain shows characteristic electrophysiologic biomarkers that are very useful for clinical management. A fundamental engineering gap for both humans and canines with epilepsy has been the challenge of obtaining continuous long-term EEG in the patients' natural environment. We are now on the cusp of a revolution where continuous long-term EEG from behaving canines and humans will be available to guide clinicians in the diagnosis and optimal treatment of their patients. Here we review some of the devices that have recently emerged for obtaining long-term EEG in ambulatory subjects living in their natural environments.

What is the optimal duration of home-video-EEG monitoring for patients with <1 seizure per day? A simulation study

Ambulatory “at home” video-EEG monitoring (HVEM) may offer a more cost-effective and accessible option as compared to traditional inpatient admissions to epilepsy monitoring units. However, home monitoring may not allow for safe tapering of anti-seizure medications (ASM). As a result, longer periods of monitoring may be necessary to capture a sufficient number of the patients' stereotypic seizures. We aimed to quantitatively estimate the necessary length of HVEM corresponding to various diagnostic scenarios in clinical practice. Using available seizure frequency statistics, we estimated the HVEM duration required to capture one, three, or five seizures on different days, by simulating 100,000 annual time-courses of seizure occurrence in adults and children with more than one and <30 seizures per month (89% of adults and 85% of children). We found that the durations of HVEM needed to record 1, 3, or 5 seizures in 80% of children were 2, 5, and 8 weeks (median 2, 12, and 21 days), respectively, and significantly longer in adults −2, 6, and 10 weeks (median 3, 14, and 26 days; p < 10⁻¹⁰ for all comparisons). Thus, longer HVEM than currently used is needed for expanding its clinical value from diagnosis of nonepileptic or very frequent epileptic events to a presurgical tool for patients with drug-resistant epilepsy. Technical developments and further studies are warranted.

Sub-scalp electroencephalography: A next-generation technique to study human neurophysiology

Sub-scalp electroencephalography (ssEEG) is emerging as a promising technology in ultra-long-term electroencephalography (EEG) recordings. Given the diversity of devices available in this nascent field, uncertainty persists about its utility in epilepsy evaluation. This review critically dissects the many proposed utilities of ssEEG devices including (1) seizure quantification, (2) seizure characterization, (3) seizure lateralization, (4) seizure localization, (5) seizure alarms, (6) seizure forecasting, (7) biomarker discovery, (8) sleep medicine, and (9) responsive stimulation. The different ssEEG devices in development have individual design philosophies with unique strengths and limitations. There are devices offering primarily unilateral recordings (24/7 EEG^TM SubQ, Neuroview^TM, Soenia® UltimateEEG™), bilateral recordings (Minder™, Epios™), and even those with responsive stimulation capability (EASEE®). We synthesize the current knowledge of these ssEEG systems. We review the (1) ssEEG devices, (2) use case scenarios, (3) challenges and (4) suggest a roadmap for ideal ssEEG designs.