Statistical characteristics of large-scale objective tonic-clonic seizure records from medical smartwatches used in daily life.

OBJECTIVE: This study aimed to assess whether population-level patterns in seizure occurrence previously observed in self-reported diaries, medical records, and electroencephalographic recordings were also present in tonic-clonic seizure (TCS) diaries produced via the combined input of a US Food and Drug Administration-cleared wristband with an artificial intelligence detection algorithm and patient self-reports. We also investigated the characteristics of patient interactions with wearable seizure alerts. METHODS: We analyzed wristband data from patients with TCSs who had at least three reported TCSs over a minimum of 90 days. We quantified TCS frequency and cycles, and the relationship between the mean and variability of monthly TCS counts. We also assessed interaction metrics such as false alarm dismissal and seizure confirmation rates. RESULTS: Applying strict criteria for usable data, we reviewed 137 490 TCSs from 3012 patients, with a median length of TCS alert records of 445 days (range = 90-1806). Analyses showed consistency between prior diary studies and the present data concerning (1) the distribution of monthly TCS frequency (median = 3.1, range = .08-26); (2) the linear relationship (slope = .79, R2 = .83) between the logarithm of the mean and the logarithm of the SD of monthly TCS frequency (L-relationship); and (iii) the prevalence of multiple coexisting seizure cycles, including circadian (84.0%), weekly (24.6%), and long-term cycles (31.1%). SIGNIFICANCE: Key population-level patterns in seizure occurrence are recapitulated in wrist-worn device recordings, supporting their validity for tracking TCS burden. Compared to other approaches, wearables can provide noninvasive, objective, long-term data, revealing cycles in seizure risk. However, improved patient engagement with wristband alerts and further validation of detection accuracy in ambulatory settings are needed. Together, these findings suggest that data from smart wristbands may be used to derive features of TCS records and, ultimately, facilitate remote monitoring and the development of personalized forecasting tools for TCS management. Our findings may not generalize to other types of seizures.

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.

Autonomic biosignals, seizure detection, and forecasting.

Wearable devices have attracted significant attention in epilepsy research in recent years for their potential to enhance patient care through improved seizure monitoring and forecasting. This narrative review presents a detailed overview of the current clinical state of the art while addressing how devices that assess autonomic nervous system (ANS) function reflect seizures and central nervous system (CNS) state changes. This includes a description of the interactions between the CNS and the ANS, including physiological and epilepsy-related changes affecting their dynamics. We first discuss technical aspects of measuring autonomic biosignals and considerations for using ANS sensors in clinical practice. We then review recent seizure detection and seizure forecasting studies, highlighting their performance and capability for seizure detection and forecasting using devices measuring ANS biomarkers. Finally, we address the field's challenges and provide an outlook for future developments.

Automated detection of tonic seizures using wearable movement sensor and artificial neural network.

Although several validated wearable devices are available for detection of generalized tonic-clonic seizures, automated detection of tonic seizures is still a challenge. In this phase 1 study, we report development and validation of an artificial neural network (ANN) model for automated detection of tonic seizures with visible clinical manifestation using a wearable wristband movement sensor (accelerometer and gyroscope). The dataset prospectively recorded for this study included 70 tonic seizures from 15 patients (seven males, age 3-46 years, median = 19 years). We trained an ANN model to detect tonic seizures. The independent test dataset comprised nocturnal recordings, including 10 tonic seizures from three patients and additional (distractor) data from three subjects without seizures. The ANN model detected nocturnal tonic seizures with visible clinical manifestation with a sensitivity of 100% (95% confidence interval = 69%-100%) and with an average false alarm rate of .16/night. The mean detection latency was 14.1 s (median = 10 s), with a maximum of 47 s. These data suggest that nocturnal tonic seizures can be reliably detected with movement sensors using ANN. Large-scale, multicenter prospective (phase 3) trials are needed to provide compelling evidence for the clinical utility of this device and detection algorithm.

Reliable detection of generalized convulsive seizures using an off-the-shelf digital watch: A multisite phase 2 study.

OBJECTIVE: The aim of this study was to develop a machine learning algorithm using an off-the-shelf digital watch, the Samsung watch (SM-R800), and evaluate its effectiveness for the detection of generalized convulsive seizures (GCS) in persons with epilepsy. METHODS: This multisite epilepsy monitoring unit (EMU) phase 2 study included 36 adult patients. Each patient wore a Samsung watch that contained accelerometer, gyroscope, and photoplethysmographic sensors. Sixty-eight time and frequency domain features were extracted from the sensor data and were used to train a random forest algorithm. A testing framework was developed that would better reflect the EMU setting, consisting of (1) leave-one-patient-out cross-validation (LOPO CV) on GCS patients, (2) false alarm rate (FAR) testing on nonseizure patients, and (3) "fixed-and-frozen" prospective testing on a prospective patient cohort. Balanced accuracy, precision, sensitivity, and FAR were used to quantify the performance of the algorithm. Seizure onsets and offsets were determined by using video-electroencephalographic (EEG) monitoring. Feature importance was calculated as the mean decrease in Gini impurity during the LOPO CV testing. RESULTS: LOPO CV results showed balanced accuracy of .93 (95% confidence interval [CI] = .8-.98), precision of .68 (95% CI = .46-.85), sensitivity of .87 (95% CI = .62-.96), and FAR of .21/24 h (interquartile range [IQR] = 0-.90). Testing the algorithm on patients without seizure resulted in an FAR of .28/24 h (IQR = 0-.61). During the "fixed-and-frozen" prospective testing, two patients had three GCS, which were detected by the algorithm, while generating an FAR of .25/24 h (IQR = 0-.89). Feature importance showed that heart rate-based features outperformed accelerometer/gyroscope-based features. SIGNIFICANCE: Commercially available wearable digital watches that reliably detect GCS, with minimum false alarm rates, may overcome usage adoption and other limitations of custom-built devices. Contingent on the outcomes of a prospective phase 3 study, such devices have the potential to provide non-EEG-based seizure surveillance and forecasting in the clinical setting.

Effects of epileptic seizures on the quality of biosignals recorded from wearables.

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

Clinical utility of ultra long-term subcutaneous electroencephalographic monitoring in drug-resistant epilepsies: a "real world" pilot study.

OBJECTIVE: This study was undertaken to assess the clinical utility, safety, and tolerability in epilepsy patients of ultra long-term monitoring with a novel subcutaneous electroencephalographic (EEG) device (sqEEG). METHODS: Five patients with drug-resistant focal epilepsy were implanted (one patient bilaterally) with sqEEG. In phase 1, we assessed sqEEG sensitivity for seizure recording by recording seizures simultaneously with scalp EEG in the epilepsy monitoring unit (EMU). sqEEG was scored either visually (v-sqEEG) or by using a semiautomatic algorithm (EpiSight; E-sqEEG). In phase 2, the patients were monitored as outpatients for 3-6 months. sqEEG data were analyzed monthly, evaluating concordance of data obtained by v-sqEEG, E-sqEEG, and patients' diaries. v-sqEEG data were used to guide treatment adjustments. sqEEG-related side effects were assessed throughout the study. RESULTS: In phase 1, v-sqEEG detected all seizures recorded in the EMU in all patients, whereas E-sqEEG was as effective in three patients. In the other two patients, E-sqEEG detected only a proportion or none of the seizures, respectively. Sensitivity of E-sqEEG depended on the ictal EEG features. In phase 2, a 100% concordance between E-sqEEG and v-sqEEG in seizure detection was observed for the same three patients as in phase 1. In the other two patients (one implanted bilaterally), effectiveness of E-sqEEG in detecting seizure as compared to v-sqEEG ranged from 0% to 83%. v-sqEEG showed that all patients reported in their diaries fewer seizures than they actually suffered. In four of five patients, v-sqEEG showed that the treatment adjustments had been ineffective or associated with a seizure increment. The only side effect was an infection at the implantation site in one patient. SIGNIFICANCE: The sqEEG system could collect reliable information on seizure activity, thus providing clinically relevant information. Sensitivity of EpiSight in detecting seizures varied across patients, depending on the ictal EEG features. sqEEG ultra long-term monitoring was feasible and well tolerated.

Focal cooling: An alternative treatment for drug-resistant epilepsy in a mesial temporal lobe epilepsy primate model-A preliminary study.

OBJECTIVE: Focal cooling is emerging as a relevant therapy for drug-resistant epilepsy (DRE). However, we lack data on its effectiveness in controlling seizures that originate in deep-seated areas like the hippocampus. We present a thermoelectric solution for focal brain cooling that specifically targets these brain structures. METHODS: A prototype implantable device was developed, including temperature sensors and a cannula for penicillin injection to create an epileptogenic zone (EZ) near the cooling tip in a non-human primate model of epilepsy. The mesial temporal lobe was targeted with repeated penicillin injections into the hippocampus. Signals were recorded from an sEEG (Stereoelectroencephalography) lead placed 2 mm from the EZ. Once the number of seizures had stabilized, focal cooling was applied, and temperature and electroclinical events were monitored using a customized detection algorithm. Tests were performed on two Macaca fascicularis monkeys at three temperatures. RESULTS: Hippocampal seizures were observed 40-120 min post-injection, their duration and frequency stabilized at around 120 min. Compared to the control condition, a reduction in the number of hippocampal seizures was observed with cooling to 21°C (Control: 4.34 seizures, SD 1.704 per 20 min vs Cooling to 21°C: 1.38 seizures, SD 1.004 per 20 min). The effect was more pronounced with cooling to 17°C, resulting in an almost 80% reduction in seizure frequency. Seizure duration and number of interictal discharges were unchanged following focal cooling. After several months of repeated penicillin injections, hippocampal sclerosis was observed, similar to that recorded in humans. In addition, seizures were identified by detecting temperature variations of 0.3°C in the EZ correlated with the start of the seizures. SIGNIFICANCE: In epilepsy therapy, the ultimate aim is total seizure control with minimal side effects. Focal cooling of the EZ could offer an alternative to surgery and to existing neuromodulation devices.

Detection of focal to bilateral tonic-clonic seizures using a connected shirt.

OBJECTIVE: This study was undertaken to develop and evaluate a machine learning-based algorithm for the detection of focal to bilateral tonic-clonic seizures (FBTCS) using a novel multimodal connected shirt. METHODS: We prospectively recruited patients with epilepsy admitted to our epilepsy monitoring unit and asked them to wear the connected shirt while under simultaneous video-electroencephalographic monitoring. Electrocardiographic (ECG) and accelerometric (ACC) signals recorded with the connected shirt were used for the development of the seizure detection algorithm. First, we used a sliding window to extract linear and nonlinear features from both ECG and ACC signals. Then, we trained an extreme gradient boosting algorithm (XGBoost) to detect FBTCS according to seizure onset and offset annotated by three board-certified epileptologists. Finally, we applied a postprocessing step to regularize the classification output. A patientwise nested cross-validation was implemented to evaluate the performances in terms of sensitivity, false alarm rate (FAR), time in false warning (TiW), detection latency, and receiver operating characteristic area under the curve (ROC-AUC). RESULTS: We recorded 66 FBTCS from 42 patients who wore the connected shirt for a total of 8067 continuous hours. The XGBoost algorithm reached a sensitivity of 84.8% (56/66 seizures), with a median FAR of .55/24 h and a median TiW of 10 s/alarm. ROC-AUC was .90 (95% confidence interval = .88-.91). Median detection latency from the time of progression to the bilateral tonic-clonic phase was 25.5 s. SIGNIFICANCE: The novel connected shirt allowed accurate detection of FBTCS with a low false alarm rate in a hospital setting. Prospective studies in a residential setting with a real-time and online seizure detection algorithm are required to validate the performance and usability of this device.

SzCORE: Seizure Community Open-Source Research Evaluation framework for the validation of electroencephalography-based automated seizure detection algorithms.

The need for high-quality automated seizure detection algorithms based on electroencephalography (EEG) becomes ever more pressing with the increasing use of ambulatory and long-term EEG monitoring. Heterogeneity in validation methods of these algorithms influences the reported results and makes comprehensive evaluation and comparison challenging. This heterogeneity concerns in particular the choice of datasets, evaluation methodologies, and performance metrics. In this paper, we propose a unified framework designed to establish standardization in the validation of EEG-based seizure detection algorithms. Based on existing guidelines and recommendations, the framework introduces a set of recommendations and standards related to datasets, file formats, EEG data input content, seizure annotation input and output, cross-validation strategies, and performance metrics. We also propose the EEG 10-20 seizure detection benchmark, a machine-learning benchmark based on public datasets converted to a standardized format. This benchmark defines the machine-learning task as well as reporting metrics. We illustrate the use of the benchmark by evaluating a set of existing seizure detection algorithms. The SzCORE (Seizure Community Open-Source Research Evaluation) framework and benchmark are made publicly available along with an open-source software library to facilitate research use, while enabling rigorous evaluation of the clinical significance of the algorithms, fostering a collective effort to more optimally detect seizures to improve the lives of people with epilepsy.

The role of trained and untrained dogs in the detection and warning of seizures.

Seizure unpredictability plays a major role in disability and decreased quality of life in people with epilepsy. Dogs have been used to assist people with disabilities and have shown promise in detecting seizures. There have been reports of trained seizure-alerting dogs (SADs) successfully detecting when a seizure is occurring or indicating imminent seizures, allowing patients to take preventative measures. Untrained pet dogs have also shown the ability to detect seizures and provide comfort and protection during and after seizures. Dogs' exceptional olfactory abilities and sensitivity to human cues could contribute to their seizure-detection capabilities. This has been supported by studies in which dogs have distinguished between epileptic seizure and non-seizure sweat samples, probably though the detection of volatile organic compounds (VOCs). However, the existing literature has limitations, with a lack of well-controlled, prospective studies and inconsistencies in reported timings of alerting behaviours. More research is needed to standardize reporting and validate the results. Advances in VOC profiling could aid in distinguishing seizure types and developing rapid and unbiased seizure detection methods. In conclusion, using dogs in epilepsy management shows considerable promise, but further research is needed to fully validate their effectiveness and potential as valuable companions for people with epilepsy.

Towards user-centered design of medical devices for SUDEP prediction and prevention: Insights from persons with epilepsy and caregivers.

OBJECTIVES: As epilepsy management medical devices emerge as potential technological solutions for prediction and prevention of sudden death in epilepsy (SUDEP), there is a gap in understanding the features and priorities that should be included in the design of these devices. This study aims to bridge the gap between current technology and emerging needs by leveraging insights from persons with epilepsy (PWE) and caregivers (CG) on current epilepsy management devices and understanding how SUDEP awareness influences preferences and design considerations for potential future solutions. METHODS: Two cross-sectional surveys were designed to survey PWE and CG on medical device design features, SUDEP awareness, and participation in medical device research. Data analysis included both qualitative thematic analysis and quantitative statistical analysis. RESULTS: The survey revealed that among 284 responses, CG were more aware of SUDEP than PWE. Comfort was identified as the primary concern regarding wearable medical devices for epilepsy management with significant differences between PWE and CG regarding acceptance and continuous use preferences. The thematic analysis identified integration with daily life, aesthetic and emotional resonance, adaptability to seizure characteristics, and user-centric design specifications as crucial factors to be considered for enhanced medical device adoption. The integration of a companion app is seen as an important tool to enhance communication and data sharing. DISCUSSION: This study reveals that while SUDEP awareness can promote the development of future SUDEP predictive and preventive medical devices, these should be designed to mitigate its impact on daily life and anxiety of both PWE and CG. Comfort and acceptance are seen as key priorities to support continuous use and are seen as a technical requirement of future medical devices for SUDEP prediction and prevention. Widespread adoption requires these technologies to be customizable to adapt to different lifestyles and social situations. A holistic approach should be used in the design of future medical devices to capture several dimensions of PWE and CG epilepsy management journey and uphold communication between healthcare professionals, PWE and CG. CONCLUSION: Data from this study highlight the importance of considering user preferences and experiences in the design of epilepsy management medical devices with potential applicability for SUDEP prediction and prevention. By employing user-centered design methods this research provides valuable insights to inform the development of future SUDEP prediction and prevention devices.

Prospective multicenter study of continuous tonic-clonic seizure monitoring on Apple Watch in epilepsy monitoring units and ambulatory environments.

OBJECTIVE: Evaluate the performance of a custom application developed for tonic-clonic seizure (TCS) monitoring on a consumer-wearable (Apple Watch) device. METHODS: Participants with a history of convulsive epileptic seizures were recruited for either Epilepsy Monitoring Unit (EMU) or ambulatory (AMB) monitoring; participants without epilepsy (normal controls [NC]) were also enrolled in the AMB group. Both EMU and AMB participants wore an Apple Watch with a research app that continuously recorded accelerometer and photoplethysmography (PPG) signals, and ran a fixed-and-frozen tonic-clonic seizure detection algorithm during the testing period. This algorithm had been previously developed and validated using a separate training dataset. All EMU convulsive events were validated by video-electroencephalography (video-EEG); AMB events were validated by caregiver reporting and follow-ups. Device performance was characterized and compared to prior monitoring devices through sensitivity, false alarm rate (FAR; false-alarms per 24 h), precision, and detection delay (latency). RESULTS: The EMU group had 85 participants (4,279 h, 19 TCS from 15 participants) enrolled across four EMUs; the AMB group had 21 participants (13 outpatient, 8 NC, 6,735 h, 10 TCS from 3 participants). All but one AMB participant completed the study. Device performance in the EMU group included a sensitivity of 100 % [95 % confidence interval (CI) 79-100 %]; an FAR of 0.05 [0.02, 0.08] per 24 h; a precision of 68 % [48 %, 83 %]; and a latency of 32.07 s [standard deviation (std) 10.22 s]. The AMB group had a sensitivity of 100 % [66-100 %]; an FAR of 0.13 [0.08, 0.24] per 24 h; a precision of 22 % [11 %, 37 %]; and a latency of 37.38 s [13.24 s]. Notably, a single AMB participant was responsible for 8 of 31 false alarms. The AMB FAR excluding this participant was 0.10 [0.07, 0.14] per 24 h. DISCUSSION: This study demonstrates the practicability of TCS monitoring on a popular consumer wearable (Apple Watch) in daily use for people with epilepsy. The monitoring app had a high sensitivity and a substantially lower FAR than previously reported in both EMU and AMB environments.

Artificial intelligence/machine learning for epilepsy and seizure diagnosis.

Accurate seizure and epilepsy diagnosis remains a challenging task due to the complexity and variability of manifestations, which can lead to delayed or missed diagnosis. Machine learning (ML) and artificial intelligence (AI) is a rapidly developing field, with growing interest in integrating and applying these tools to aid clinicians facing diagnostic uncertainties. ML algorithms, particularly deep neural networks, are increasingly employed in interpreting electroencephalograms (EEG), neuroimaging, wearable data, and seizure videos. This review discusses the development and testing phases of AI/ML tools, emphasizing the importance of generalizability and interpretability in medical applications, and highlights recent publications that demonstrate the current and potential utility of AI to aid clinicians in diagnosing epilepsy. Current barriers of AI integration in patient care include dataset availability and heterogeneity, which limit studies' quality, interpretability, comparability, and generalizability. ML and AI offer substantial promise in improving the accuracy and efficiency of epilepsy diagnosis. The growing availability of diverse datasets, enhanced processing speed, and ongoing efforts to standardize reporting contribute to the evolving landscape of AI applications in clinical care.

Update review on SUDEP: Risk assessment, background & seizure detection devices.

This review focusses on sudden unexpected death in epilepsy patients (SUDEP) and incorporates risk stratification (through SUDEP risk factors and SUDEP risk scores), hypotheses on the mechanism of SUDEP and eligible seizure detection devices (SDDs) for further SUDEP prevention studies. The main risk factors for SUDEP are the presence and the frequency of generalized tonic-clonic seizures (GTC). In Swedish population-based case control study, the Odds ratio of the presence of GTC in the absence of bedroom sharing is 67. SUDEP risk scoring systems express a score that represents the cumulative presence of SUDEP risk factors, but not the exact effect of their combination. We describe 4 of the available scoring systems: SUDEP-7 inventory, SUDEP-3 inventory, SUDEP-ClinicAl Risk scorE (SUDEP-CARE score) and Kempenhaeghe SUDEP risk score. Although they all include GTC, their design is often different. Three of 4 scoring systems were validated (SUDEP-7 inventory, SUDEP-3 inventory and SUDEP-CARE score). None of the available scoring systems has been sufficiently validated for the use in a general epilepsy population. Plausible mechanisms of SUDEP are discussed. In the MORTEMUS-study (Mortality in Epilepsy Monitoring Unit Study), SUDEP was a postictal cardiorespiratory arrest after a GTC. The parallel respiratory and cardiac dysfunction in SUDEP suggests a central dysfunction of the brainstem centers that are involved in the control of respiration and heart rhythm. In the (consequent) adenosine serotonin hypotheses SUDEP occurs when a postictal adenosine-mediated respiratory depression is not compensated by the effect of serotonin. Other (adjuvant) mechanisms and factors are discussed. Seizure detection devices (SDDs) may help to improve nocturnal supervision. Five SDDs have been validated in phase 3 studies for the detection of TC: Seizure Link®, Epi-Care®, NightWatch, Empatica, Nelli®. They have demonstrated a sensitivity of at least 90 % combined with an acceptable false positive alarm rate. It has not yet been proven that the use will actually lead to SUDEP prevention, but clinical experience supports their effectiveness.

Evaluation of Commercially Available Seizure Detection Wearables in Canada: Current Evidence.

Wearable-based seizure detection devices hold promise in reducing seizure-related adverse events and relieving the daily stress experienced by people with epilepsy. In this work, we present the latest evidence regarding the performance of three seizure detection wearables (eight studies) commercially available in Canada to provide guidance to clinicians. Overall, their ability to detect focal-to-bilateral and/or generalized tonic-clonic seizures ranges between 21.0% and 98.15% in sensitivity, with the 24h false alarm rates ranging from 0 to 1.28. While performance in epilepsy monitoring units show promise, the lack of evidence in outpatient settings precludes strong recommendations for their use in daily life.

Residual Seizure Rate of Intermittent Inpatient EEG Compared to a Continuous EEG Model.

BACKGROUND: Subclinical seizures are common in hospitalized patients and require electroencephalography (EEG) for detection and intervention. At our institution, continuous EEG (cEEG) is not available, but intermittent EEGs are subject to constant live interpretation. As part of quality improvement (QI), we sought to estimate the residual missed seizure rate at a typical quaternary Canadian health care center without cEEG. METHODS: We calculated residual risk percentages using the clinically validated 2HELPS2B score to risk-stratify EEGs before deriving a risk percentage using a MATLAB calculator which modeled the risk decay curve for each recording. We generated a range of estimated residual seizure rates depending on whether a pre-cEEG screening EEG was simulated, EEGs showing seizures were included, or repeat EEGs on the same patient were excluded. RESULTS: Over a 4-month QI period, 499 inpatient EEGs were scored as low (n = 125), medium (n = 123), and high (n = 251) seizure risk according to 2HELPS2B criteria. Median recording duration was 1:00:06 (interquartile range, IQR 30:40-2:21:10). The model with highest residual seizure rate included recordings with confirmed electrographic seizures (median 20.83%, IQR 20.6-26.6%), while the model with lowest residual seizure rate was in seizure-free recordings (median 10.59%, IQR 4%-20.6%). These rates were significantly higher than the benchmark 5% miss-rate threshold set by 2HELPS2B (p<0.0001). CONCLUSIONS: We estimate that intermittent inpatient EEG misses 2-4 times more subclinical seizures than the 2HELPS2B-determined acceptable 5% seizure miss-rate threshold for cEEG. Future research is needed to determine the impact of potentially missed seizures on clinical care.

Patient Safety in Canadian Epilepsy Monitoring Units: A Survey of Current Practices.

BACKGROUND: Guidelines on epilepsy monitoring unit (EMU) standards have been recently published. We aimed to survey Canadian EMUs to describe the landscape of safety practices and compare these to the recommendations from the new guidelines. METHODS: A 34-item survey was created by compiling questions on EMU structure, patient monitoring, equipment, personnel, standardized protocol use, and use of injury prevention tools. The questionnaire was distributed online to 24 Canadian hospital centers performing video-EEG monitoring (VEM) in EMUs. Responses were tabulated and descriptively summarized. RESULTS: In total, 26 EMUs responded (100% response rate), 50% of which were adult EMUs. EMUs were on average active for 23.4 years and had on average 3.6 beds. About 81% of respondents reported having a dedicated area for VEM, and 65% reported having designated EMU beds. Although a video monitoring station was available in 96% of EMUs, only 48% of EMUs provided continuous observation of patients (video and/or physical). A total of 65% of EMUs employed continuous heart monitoring. The technologist-to-patient ratio was 1:1-2 in 52% of EMUs during the day. No technologist supervision was most often reported in the evening and at night. Nurse-to-EMU-patient ratio was mostly 1:1-4 independent of the time of day. Consent forms were required before admission in 27% of EMUs. CONCLUSION: Canadian EMUs performed decently in terms of there being dedicated space for VEM, continuous heart monitoring, and adequate nurse-to-patient ratios. Other practices were quite variable, and adjustments should be made on a case-by-case basis to adhere to the latest guidelines.

Automated Seizure Detection Based on State-Space Model Identification.

In this study, we developed a machine learning model for automated seizure detection using system identification techniques on EEG recordings. System identification builds mathematical models from a time series signal and uses a small number of parameters to represent the entirety of time domain signal epochs. Such parameters were used as features for the classifiers in our study. We analyzed 69 seizure and 55 non-seizure recordings and an additional 10 continuous recordings from Thomas Jefferson University Hospital, alongside a larger dataset from the CHB-MIT database. By dividing EEGs into epochs (1 s, 2 s, 5 s, and 10 s) and employing fifth-order state-space dynamic systems for feature extraction, we tested various classifiers, with the decision tree and 1 s epochs achieving the highest performance: 96.0% accuracy, 92.7% sensitivity, and 97.6% specificity based on the Jefferson dataset. Moreover, as the epoch length increased, the accuracy dropped to 94.9%, with a decrease in sensitivity to 91.5% and specificity to 96.7%. Accuracy for the CHB-MIT dataset was 94.1%, with 87.6% sensitivity and 97.5% specificity. The subject-specific cases showed improved results, with an average of 98.3% accuracy, 97.4% sensitivity, and 98.4% specificity. The average false detection rate per hour was 0.5 ± 0.28 in the 10 continuous EEG recordings. This study suggests that using a system identification technique, specifically, state-space modeling, combined with machine learning classifiers, such as decision trees, is an effective and efficient approach to automated seizure detection.

Parallel Ictal-Net, a Parallel CNN Architecture with Efficient Channel Attention for Seizure Detection.

Around 70 million people worldwide are affected by epilepsy, a neurological disorder characterized by non-induced seizures that occur at irregular and unpredictable intervals. During an epileptic seizure, transient symptoms emerge as a result of extreme abnormal neural activity. Epilepsy imposes limitations on individuals and has a significant impact on the lives of their families. Therefore, the development of reliable diagnostic tools for the early detection of this condition is considered beneficial to alleviate the social and emotional distress experienced by patients. While the Bonn University dataset contains five collections of EEG data, not many studies specifically focus on subsets D and E. These subsets correspond to EEG recordings from the epileptogenic zone during ictal and interictal events. In this work, the parallel ictal-net (PIN) neural network architecture is introduced, which utilizes scalograms obtained through a continuous wavelet transform to achieve the high-accuracy classification of EEG signals into ictal or interictal states. The results obtained demonstrate the effectiveness of the proposed PIN model in distinguishing between ictal and interictal events with a high degree of confidence. This is validated by the computing accuracy, precision, recall, and F1 scores, all of which consistently achieve around 99% confidence, surpassing previous approaches in the related literature.