Seizure detection using the wristband accelerometer, gyroscope, and surface electromyogram signals based on in-hospital and out-of-hospital dataset

OBJECTIVE

Wearable devices are effective for detecting generalized tonic-clonic seizures (GTCS). However, many daily activities are often misclassified as GTCS, leading to a decline in user confidence. This study recommends utilizing wristband three-axis accelerometer (ACC), three-axis gyroscope (GYRO), and surface electromyography (sEMG) signals for GTCS detection and presents a novel seizure detection algorithm that offers high sensitivity and a reduced false alarm rate (FAR).

METHODS

Inpatients with epilepsy and out-of-hospital healthy subjects were recruited and required to wear a wristband device to collect wristband signals. The proposed algorithm comprises five steps: preprocessing, motion filtering, feature extraction, classification, and postprocessing. The variations in performance across different signal combinations were compared. Additionally, the impact of training the model using only inpatient data versus the complete dataset on the algorithm's performance was also investigated.

RESULTS

Wristband signals were collected from 45 patients and 30 healthy subjects, encompassing a total of 3367.3 h and including 60 GTCS. The proposed algorithm achieved 100 % sensitivity and a FAR of 0.1070/24 h. It demonstrated higher sensitivity and lower FAR compared to combinations with fewer signal modalities. In addition, the model trained on only in-hospital data demonstrates high sensitivity (98.33 %) and high FAR (0.9845/24 h).

SIGNIFICANCE

The algorithm proposed for detecting GTCS using wristband ACC, GYRO, and sEMG signals achieved encouraging results, demonstrating the feasibility of this signal combination. Furthermore, incorporating out-of-hospital data into model training proved to be an effective solution for reducing FAR, which could facilitate the clinical application of seizure detection algorithms.

Closed-Loop Implantable Neurostimulators for Individualized Treatment of Intractable Epilepsy: A Review of Recent Developments, Ongoing Challenges, and Future Opportunities

Driven by its proven therapeutic efficacy in treating movement disorders and psychiatric conditions, neurostimulation has emerged as a promising intervention for intractable epilepsy. Researchers envision an advanced implantable device capable of long-term neuronal monitoring, high spatio-temporal resolution data processing, and timely responsive neurostimulation upon seizure detection. However, the stringent energy constraints of implantable devices and significant inter-patient variability in neural activity pose substantial challenges and opportunities for biomedical circuits and systems researchers. For seizure detection, various ASIC solutions employing both deterministic and data-driven algorithms have been developed. These solutions leverage a subset of numerous signal features (spanning time and frequency domains) and classifiers (such as SVMs, DNNs, SNNs) to achieve notable success in terms of detection accuracy, latency, and energy efficiency. Implementations vary widely in computational approaches (digital, mixed-signal, analog, spike-based), training strategies (online versus offline), and application targets (patient-specific versus cross-patient). In terms of treatment, recent efforts have focused on the personalization of stimulation waveforms to enhance therapeutic efficacy. This personalization faces complex challenges, including a limited understanding of how stimulation parameters influence neuronal activity, the lack of a comprehensive brain model to capture its intricate electrochemical dynamics, and recording neural signals in the presence of stimulation artifacts. This review provides a comprehensive overview of the field, detailing the foundational principles, recent advancements, and ongoing challenges in enhancing the diagnostic accuracy, treatment efficacy, and energy efficiency of implantable patient-optimized neurostimulators. We also discuss potential future directions, emphasizing the need for standardized performance metrics, advanced computational models, and adaptive stimulation protocols to realize the full potential of this transformative technology.

Multilevel attention mechanism for motion fatigue recognition based on sEMG and ACC signal fusion

This study aims to develop a cost-effective and reliable motion monitoring device capable of comprehensive fatigue analysis. It achieves this objective by integrating surface electromyography (sEMG) and accelerometer (ACC) signals through a feature fusion strategy. The study introduces a multi-level attention mechanism for classification, leveraging convolutional neural networks (CNNs). The preprocessing phase involves a local feature attention mechanism that enhances local waveform features using the amplitude envelope. A dual-scale attention mechanism, operating at both channel and neuron levels, is employed to enhance the model's learning from high-dimensional fused data, improving feature extraction and generalization. The local feature attention mechanism significantly improves the model's classification accuracy and convergence, as demonstrated in ablation experiments. The model, optimized with multi-level attention mechanisms, excels in accuracy and generalization, particularly in handling data with pseudo-artifacts. Computational analysis indicates that the proposed optimization algorithm has minimal impact on CNN's training and testing times. The study achieves recognition accuracies of 92.52%, 92.38%, and 92.30%, as well as F1-scores of 91.92%, 92.13%, and 92.29% for the three fatigue states, affirming its reliability. This research provides technical support for the development of affordable and dependable wearable motion monitoring devices.

Learning Robust Representations of Tonic-Clonic Seizures With Cyclic Transformer

Tonic-clonic seizures (TCSs) pose a significant risk for sudden unexpected death in epilepsy (SUDEP). Previous research has highlighted the potential of multimodal wearable seizure detection systems in accurately detecting TCSs through continuous monitoring, enabling timely alarms and potentially preventing SUDEP. However, such multimodal systems carry a higher risk of sensor malfunction. In this paper, we propose a cyclic transformer approach to address these challenges. The cyclic transformer learns a robust representation by performing circular modal translations between the source and target modalities. It leverages back-translation as regularization technique to enhance the discriminative power of the learned representation. Notably, the proposed cyclic transformer is trained on paired multimodal data but requires only a single source modality during deployment. This characteristic ensures the robustness of the cyclic transformer to perturbations or missing information in the target modality. Experimental results demonstrate that the proposed cyclic transformer achieves competitive performance compared with existing multimodal systems. While both approaches were trained using EEG and EMG data, the cyclic transformer exclusively employs EEG data for testing, diverging from the state-of-the-art's utilization of both EEG and EMG data during test. This showcases the effectiveness of the cyclic transformer in multimodal TCSs detection, offering a promising approach for enhancing the accuracy and robustness of seizure detection systems while mitigating the risks associated with sensor malfunction.

Automated analysis and detection of epileptic seizures in video recordings using artificial intelligence

Introduction

Automated seizure detection promises to aid in the prevention of SUDEP and improve the quality of care by assisting in epilepsy diagnosis and treatment adjustment.

Methods

In this phase 2 exploratory study, the performance of a contactless, marker-free, video-based motor seizure detection system is assessed, considering video recordings of patients (age 0-80 years), in terms of sensitivity, specificity, and Receiver Operating Characteristic (ROC) curves, with respect to video-electroencephalographic monitoring (VEM) as the medical gold standard. Detection performances of five categories of motor epileptic seizures (tonic-clonic, hyperkinetic, tonic, unclassified motor, automatisms) and psychogenic non-epileptic seizures (PNES) with a motor behavioral component lasting for >10 s were assessed independently at different detection thresholds (rather than as a categorical classification problem). A total of 230 patients were recruited in the study, of which 334 in-scope (>10 s) motor seizures (out of 1,114 total seizures) were identified by VEM reported from 81 patients. We analyzed both daytime and nocturnal recordings. The control threshold was evaluated at a range of values to compare the sensitivity (n = 81 subjects with seizures) and false detection rate (FDR) (n = all 230 subjects).

Results

At optimal thresholds, the performance of seizure groups in terms of sensitivity (CI) and FDR/h (CI): tonic-clonic- 95.2% (82.4, 100%); 0.09 (0.077, 0.103), hyperkinetic- 92.9% (68.5, 98.7%); 0.64 (0.59, 0.69), tonic- 78.3% (64.4, 87.7%); 5.87 (5.51, 6.23), automatism- 86.7% (73.5, 97.7%); 3.34 (3.12, 3.58), unclassified motor seizures- 78% (65.4, 90.4%); 4.81 (4.50, 5.14), and PNES- 97.7% (97.7, 100%); 1.73 (1.61, 1.86). A generic threshold recommended for all motor seizures under study asserted 88% sensitivity and 6.48 FDR/h.

Discussion

These results indicate an achievable performance for major motor seizure detection that is clinically applicable for use as a seizure screening solution in diagnostic workflows.

Seizure detection based on wearable devices: A review of device, mechanism, and algorithm

With sudden and unpredictable nature, seizures lead to great risk of the secondary damage, status epilepticus, and sudden unexpected death in epilepsy. Thus, it is essential to use a wearable device to detect seizure and inform patients' caregivers for assistant to prevent or relieve adverse consequence. In this review, we gave an account of the current state of the field of seizure detection based on wearable devices from three parts: devices, physiological activities, and algorithms. Firstly, seizure monitoring devices available in the market primarily involve wristband-type devices, patch-type devices, and armband-type devices, which are able to detect motor seizures, focal autonomic seizures, or absence seizures. Secondly, seizure-related physiological activities involve the discharge of brain neurons presented, autonomous nervous activities, and motor. Plenty of studies focus on features from one signal, while it is a lack of evidences about the change of signal coupling along with seizures. Thirdly, the seizure detection algorithms developed from simple threshold method to complicated machine learning and deep learning, aiming at distinguish seizures from normal events. After understanding of some preliminary studies, we will propose our own thought for future development in this field.

Automated seizure detection with non-invasive wearable devices: A systematic review and meta-analysis

Objective

This study was undertaken to review the reported performance of noninvasive wearable devices in detecting epileptic seizures and psychogenic nonepileptic seizures (PNES).

Methods

We conducted a systematic review and meta‐analysis of studies reported up to November 15, 2021. We included studies that used video‐electroencephalographic (EEG) monitoring as the gold standard to determine the sensitivity and false alarm rate (FAR) of noninvasive wearables for automated seizure detection.

Results

Twenty‐eight studies met the criteria for the systematic review, of which 23 were eligible for meta‐analysis. These studies (1269 patients in total, median recording time = 52.9 h per patient) investigated devices for tonic–clonic seizures using wrist‐worn and/or ankle‐worn devices to measure three‐dimensional accelerometry (15 studies), and/or wearable surface devices to measure electromyography (eight studies). The mean sensitivity for detecting tonic–clonic seizures was .91 (95% confidence interval [CI] = .85–.96, I² = 83.8%); sensitivity was similar between the wrist‐worn (.93) and surface devices (.90). The overall FAR was 2.1/24 h (95% CI = 1.7–2.6, I² = 99.7%); FAR was higher in wrist‐worn (2.5/24 h) than in wearable surface devices (.96/24 h). Three of the 23 studies also detected PNES; the mean sensitivity and FAR from these studies were 62.9% and .79/24 h, respectively. Four studies detected both focal and tonic–clonic seizures, and one study detected focal seizures only; the sensitivities ranged from 31.1% to 93.1% in these studies.

Significance

Reported noninvasive wearable devices had high sensitivity but relatively high FARs in detecting tonic–clonic seizures during limited recording time in a video‐EEG setting. Future studies should focus on reducing FAR, detection of other seizure types and PNES, and longer recording in the community.

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

BACKGROUND

Video electroencephalography recordings, routinely used in epilepsy monitoring units, are the gold standard for monitoring epileptic seizures. However, monitoring is also needed in the day-to-day lives of people with epilepsy, where video electroencephalography is not feasible. Wearables could fill this gap by providing patients with an accurate log of their seizures.

OBJECTIVE

Although there are already systems available that provide promising results for the detection of tonic-clonic seizures (TCSs), research in this area is often limited to detection from 1 biosignal modality or only during the night when the patient is in bed. The aim of this study is to provide evidence that supervised machine learning can detect TCSs from multimodal data in a new data set during daytime and nighttime.

METHODS

An extensive data set of biosignals from a multimodal watch worn by people with epilepsy was recorded during their stay in the epilepsy monitoring unit at 2 European clinical sites. From a larger data set of 243 enrolled participants, those who had data recorded during TCSs were selected, amounting to 10 participants with 21 TCSs. Accelerometry and electrodermal activity recorded by the wearable device were used for analysis, and seizure manifestation was annotated in detail by clinical experts. Ten accelerometry and 3 electrodermal activity features were calculated for sliding windows of variable size across the data. A gradient tree boosting algorithm was used for seizure detection, and the optimal parameter combination was determined in a leave-one-participant-out cross-validation on a training set of 10 seizures from 8 participants. The model was then evaluated on an out-of-sample test set of 11 seizures from the remaining 2 participants. To assess specificity, we additionally analyzed data from up to 29 participants without TCSs during the model evaluation.

RESULTS

In the leave-one-participant-out cross-validation, the model optimized for sensitivity could detect all 10 seizures with a false alarm rate of 0.46 per day in 17.3 days of data. In a test set of 11 out-of-sample TCSs, amounting to 8.3 days of data, the model could detect 10 seizures and produced no false positives. Increasing the test set to include data from 28 more participants without additional TCSs resulted in a false alarm rate of 0.19 per day in 78 days of wearable data.

CONCLUSIONS

We show that a gradient tree boosting machine can robustly detect TCSs from multimodal wearable data in an original data set and that even with very limited training data, supervised machine learning can achieve a high sensitivity and low false-positive rate. This methodology may offer a promising way to approach wearable-based nonconvulsive seizure detection.

Differentiation of Epileptic and Psychogenic Nonepileptic Seizures Using Single-Channel Surface Electromyography

PURPOSE

Epileptic seizures (ES) and psychogenic nonepileptic seizures (PNES) are difficult to differentiate when based on a patient's self-reported symptoms. This study proposes review of objective data captured by a surface electromyography (sEMG) wearable device for classification of events as ES or PNES. This may help clinicians accurately identify ES and PNES.

METHODS

Seventy-one subjects were prospectively enrolled across epilepsy monitoring units at VA Epilepsy Centers of Excellence. Subjects were concomitantly monitored using video EEG and a wearable sEMG epilepsy monitor, the Sensing Portable sEmg Analysis Characterization (SPEAC) System. Three epileptologists independently classified ES and PNES that contained upper extremity motor activity based on video EEG. The sEMG data from those events were automatically processed to provide a seizure score for event classification. After brief training (60 minutes), the sEMG data were reviewed by a separate group of four epileptologists to independently classify events as ES or PNES.

RESULTS

According to video EEG review, 17 subjects experienced 34 events (15 ES and 19 PNES with upper extremity motor activity). The automated process correctly classified 87% of ES (positive predictive value = 88%, negative predictive value = 76%) and 79% of PNES, and the expert reviewers correctly classified 77% of ES (positive predictive value = 94%, negative predictive value = 84%) and 96% of PNES. The automated process and the expert reviewers correctly classified 100% of tonic-clonic seizures as ES, and 71 and 50%, respectively, of non-tonic-clonic ES.

CONCLUSIONS

Automated and expert review, particularly in combination, of sEMG captured by a wearable seizure monitor (SPEAC System) may be able to differentiate ES (especially tonic-clonic) and PNES with upper extremity motor activity.

A patient-independent classification system for onset detection of seizures

Seizures are the most common brain dysfunction. Electroencephalography (EEG) is required for their detection and treatment initially. Studies show that if seizures are detected at their early stage, instant and effective treatment can be given to the patients. In this paper, an automated system for seizure onset detection is proposed. As the power spectrum of normal person's EEG and EEG of someone with epilepsy is plotted, powers present at different frequencies are found to be different for both. The proposed algorithm utilizes this frequency discrimination property of EEG with some statistical features to detect the seizure onset using simple linear classifier. The tests conducted on EEG data of 30 patients, obtained from the two different datasets, show the presence of all 183 seizures with mean latency of 0.9 s and 1.02 false detections per hour. The main contribution of this study is the use of simple features and classifier in the field of seizures onset detection that reduces the computational complexity of the algorithm. Also, the classifier used is patient independent. This patient independency in the classification system would be helpful in the implementation of the proposed algorithm to develop an online detection system.

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 (ILAE) and the International Federation of Clinical Neurophysiology (IFCN) 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 the 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 a 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.

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.

Using sEMG to identify seizure semiology of motor seizures

PURPOSE

Accurate characterization and quantification of seizure types are critical for optimal pharmacotherapy in epilepsy patients. Technological advances have made it possible to continuously monitor physiological signals within or outside the hospital setting. This study tested the utility of single-channel surface-electromyography (sEMG) for characterization of motor epileptic seizure semiology.

METHODS

Seventy-one subjects were prospectively enrolled where vEEG and sEMG were simultaneously recorded. Three epileptologists independently identified and classified seizure events with upper-extremity (UE) motor activity by reviewing vEEG, serving as a clinical standard. Surface EMG recorded during the events identified by the clinical standard were evaluated using automated classification methods and expert review by a second group of three independent epileptologists (blinded to the vEEG data). Surface EMG classification categories included: tonic-clonic (TC), tonic only, clonic only, or other motor seizures. Both automated and expert review of sEMG was compared to the clinical standard.

RESULTS

Twenty subjects experienced 47 motor seizures. Automated sEMG event classification methods accurately classified 72 % (95 % CI [0.57, 0.84]) of events (15/18 TC seizures, 5/9 tonic seizures, 1/3 clonic seizures, and 13/17 other seizures). Three independent reviewers' majority-rule analysis of sEMG correctly classified 81 % (95 % CI [0.67, 0.91]) of events (16/18 TC seizures, 8/9 tonic seizures, 1/3 clonic seizures, and 13/17 other manifestations).

CONCLUSIONS

Continuous monitoring of sEMG data provides an objective measure to evaluate motor seizure activity. Using sEMG from a wearable monitor recorded from the biceps, automated and expert review may be used to characterize the semiology of events with UE motor activity, particularly TC and tonic seizures.

Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor data

This paper investigates the feasibility of using non-cerebral, time-series data to detect epileptic seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 not noted, mean age 36.17 yrs), five of whom had a total of seven seizures. Patients were monitored in an inpatient setting using standard video-electroencephalography (vEEG), while also wearing sensors monitoring electrocardiography, electrodermal activity, electromyography, accelerometry, and audio signals (vocalizations). A systematic and detailed study was conducted to identify the sensors and the features derived from the non-cerebral sensors that contribute most significantly to separability of data acquired during seizures from non-seizure data. Post-processing of the data using linear discriminant analysis (LDA) shows that seizure data are strongly separable from non-seizure data based on features derived from the signals recorded. The mean area under the receiver operator characteristic (ROC) curve for each individual patient that experienced a seizure during data collection, calculated using LDA, was 0.9682. The features that contribute most significantly to seizure detection differ for each patient. The results show that a multimodal approach to seizure detection using the specified sensor suite is promising in detecting seizures with both sensitivity and specificity. Moreover, the study provides a means to quantify the contribution of each sensor and feature to separability. Development of a non-electroencephalography (EEG) based seizure detection device would give doctors a more accurate seizure count outside of the clinical setting, improving treatment and the quality of life of epilepsy patients.

Ictal quantitative surface electromyography correlates with postictal EEG suppression

OBJECTIVE

To test the hypothesis that neurophysiologic biomarkers of muscle activation during convulsive seizures reveal seizure severity and to determine whether automatically computed surface EMG parameters during seizures can predict postictal generalized EEG suppression (PGES), indicating increased risk for sudden unexpected death in epilepsy. Wearable EMG devices have been clinically validated for automated detection of generalized tonic-clonic seizures. Our goal was to use quantitative EMG measurements for seizure characterization and risk assessment.

METHODS

Quantitative parameters were computed from surface EMGs recorded during convulsive seizures from deltoid and brachial biceps muscles in patients admitted to long-term video-EEG monitoring. Parameters evaluated were the durations of the seizure phases (tonic, clonic), durations of the clonic bursts and silent periods, and the dynamics of their evolution (slope). We compared them with the duration of the PGES.

RESULTS

We found significant correlations between quantitative surface EMG parameters and the duration of PGES (p < 0.001). Stepwise multiple regression analysis identified as independent predictors in deltoid muscle the duration of the clonic phase and in biceps muscle the duration of the tonic-clonic phases, the average silent period, and the slopes of the silent period and clonic bursts. The surface EMG-based algorithm identified seizures at increased risk (PGES ≥20 seconds) with an accuracy of 85%.

CONCLUSIONS

Ictal quantitative surface EMG parameters correlate with PGES and may identify seizures at high risk.

CLASSIFICATION OF EVIDENCE

This study provides Class II evidence that during convulsive seizures, surface EMG parameters are associated with prolonged postictal generalized EEG suppression.

Wearable devices for seizure detection: Is it time to translate into our clinical practice?

With the exponential development of mobile health technologies over the past ten years, there has been a growing interest in the potential applications in the field of epilepsy, and specifically for seizure detection. Better detection of seizures is probably one of the best ways to improve patient safety. Overall, we are observing an exponential increase in the number of non-EEG based seizure detection systems and a progressive homogenization of their evaluation procedures. Most importantly, the properties of these devices for detection of tonic-clonic seizures are now very interesting, both in terms of sensitivity and in terms of false-alarm rates. Accordingly, we might expect that these be used in clinical practice in the near future, especially in patients at high risk of seizure-related injuries or sudden unexpected death in epilepsy (SUDEP).

The role of devices in managing risk

Over the last few years, there has been significant expansion of wearable technologies and devices into the health sector, including for conditions such as epilepsy. Although there is significant potential to benefit patients, there is a paucity of well-conducted scientific research in order to inform patients and healthcare providers of the most appropriate technology. In addition to either directly or indirectly identifying seizure activity, the ideal device should improve quality of life and reduce the risk of sudden unexpected death in epilepsy (SUDEP). Devices typically monitor a number of parameters including electroencephalographic (EEG), cardiac, and respiratory patterns and can detect movement, changes in skin conductance, and muscle activity. Multimodal devices are emerging with improved seizure detection rates and reduced false positive alarms. While convulsive seizures are reliably identified by most unimodal and multimodal devices, seizures associated with no, or minimal, movement are frequently undetected. The vast majority of current devices detect but do not actively intervene. At best, therefore, they indicate the presence of seizure activity in order to accurately ascertain true seizure frequency or facilitate intervention by others, which may, nevertheless, impact the rate of SUDEP. Future devices are likely to both detect and intervene within an autonomous closed-loop system tailored to the individual and by self-learning from the analysis of patient-specific parameters. The formulation of standards for regulatory bodies to validate seizure detection devices is also of paramount importance in order to confidently ascertain the performance of a device; and this will be facilitated by the creation of a large, open database containing multimodal annotated data in order to test device algorithms. This paper is for the Special Issue: Prevent 21: SUDEP Summit - Time to Listen.

Non-electroencephalography-based seizure detection

PURPOSE OF REVIEW

There is need for automated seizure detection using mobile or wearable devices, for objective seizure documentation and decreasing morbidity and mortality associated with seizures. Due to technological development, a high number of articles have addressed non-electroencephalography (EEG)-based seizure detection. However, the quality of study-design and reporting is extremely heterogeneous. We aimed at giving the reader a clear picture on the current state of seizure detection, describing the level of evidence behind the various devices.

RECENT FINDINGS

Fifteen studies of phase-2 or above, demonstrated that non-EEG-based devices detected generalized tonic-clonic seizures (GTCS) with high sensitivity (≥90%) and low false alarm rate (FAR) (down to 0.2/day). We found limited evidence for detection of motor seizures other than GTCS, mostly from subgroups in larger studies, targeting GTCS. There is little evidence for non-EEG-based detection of nonmotor seizures: sensitivity is low (19-74%) with extremely high FAR (50-216/day).

SUMMARY

Detection of GTCS is reliable and there are several, validated devices on the market. However, detection of other seizure types needs further research.

Seizure detection devices for use in antiseizure medication clinical trials: A systematic review

OBJECTIVE

This study characterizes the current capabilities of seizure detection device (SDD) technology and evaluates the fitness of these devices for use in anti-seizure medication (ASM) clinical trials.

METHODS

Through a systematic literature review, 36 wireless SDDs featured in published device validation studies were identified. Each device's seizure detection capabilities that addressed ASM clinical trial primary endpoint measurement needs were cataloged.

RESULTS

The two most common types of seizures targeted by ASMs in clinical trials are generalized tonic-clonic (GTC) seizures and focal with impaired awareness (FIA) seizures. The Brain Sentinel SPEAC achieved the highest performance for the detection of GTC seizures (F₁-score = 0.95). A non-commercial wireless EEG device achieved the highest performance for the detection of FIA seizures (F₁-score = 0.88).

DISCUSSION

A preliminary assessment of device capabilities for measuring selected ASM clinical trial secondary endpoints was performed. The need to address key limitations in validation studies is highlighted in order to support future assessments of SDD fitness for ASM clinical trial use. In tandem, a stepwise framework to streamline device testing is put forth. These suggestions provide a starting point for establishing SDD reporting requirements before device integration into ASM clinical trials.

Detection of convulsive seizures using surface electromyography

Bilateral (generalized) tonic-clonic seizures (TCS) increase the risk of sudden unexpected death in epilepsy (SUDEP), especially when patients are unattended. In sleep, TCS often remain unnoticed, which can result in suboptimal treatment decisions. There is a need for automated detection of these major epileptic seizures, using wearable devices. Quantitative surface electromyography (EMG) changes are specific for TCS and characterized by a dynamic evolution of low- and high-frequency signal components. Algorithms targeting increase in high-frequency EMG signals constitute biomarkers of TCS; they can be used both for seizure detection and for differentiating TCS from convulsive nonepileptic seizures. Two large-scale, blinded, prospective studies demonstrated the accuracy of wearable EMG devices for detecting TCS with high sensitivity (76%-100%). The rate of false alarms (0.7-2.5/24 h) needs further improvement. This article summarizes the pathophysiology of muscle activation during convulsive seizures and reviews the published evidence on the accuracy of EMG-based seizure detection.

Standards for testing and clinical validation of seizure detection devices

To increase the quality of studies on seizure detection devices, we propose standards for testing and clinical validation of such devices. We identified 4 key features that are important for studies on seizure detection devices: subjects, recordings, data analysis and alarms, and reference standard. For each of these features, we list the specific aspects that need to be addressed in the studies, and depending on these, studies are classified into 5 phases (0-4). We propose a set of outcome measures that need to be reported, and we propose standards for reporting the results. These standards will help in designing and reporting studies on seizure detection devices, they will give readers clear information on the level of evidence provided by the studies, and they will help regulatory bodies in assessing the quality of the validation studies. These standards are flexible, allowing classification of the studies into one of the 5 phases. We propose actions that can facilitate development of novel methods and devices.

Onset Detection of Epileptic Seizures From Accelerometry Signal

Epileptic seizures are the result of any abnormal asynchronous firing of cortical neurons. Seizures are abrupt and pose a risk of injury and fatal harm to the patient. Epilepsy affects patients quality of life (QOL) and imposes financial, social, and physical burden on the patient. The unpredictability associated with seizures further adds to the reduced QOL and increases dependence on caregivers and family members. A seizure triggered alarm system can reduce the risk of seizure-related injuries and aid in improving patient's QOL. This study presents real-time onset detection of seizures from accelerometry signal. An automated approach based on statistical machine learning is employed to learn the onset of seizures. To search for the optimal parameter that simultaneously maximizes detection sensitivity (sens) while minimizing false alarm rate (FAR) and latency, the epoch length is varied from $t=\{1,~10s\}$. Linear and non-linear time-varying dynamical patterns were extracted from every epoch using Poincaré plot analysis. The correlation patterns were learned using a kernalized support vector data descriptor. The preliminary analysis on accelerometry data collected from 8 epileptic patients with 9 generalized tonicclonic seizures (GTCS) shows promising results. The proposed algorithm detected all GTCS events (sens: 100%, FAR: 1. 09/24h) at 8s from onset. The proposed algorithm can lead to a sensitive, specific, and a relatively short-latency detection system for real-time remote monitoring of epileptic patients.

Automatic Computer-Based Detection of Epileptic Seizures

Automatic computer-based seizure detection and warning devices are important for objective seizure documentation, for SUDEP prevention, to avoid seizure related injuries and social embarrassments as a consequence of seizures, and to develop on demand epilepsy therapies. Automatic seizure detection systems can be based on direct analysis of epileptiform discharges on scalp-EEG or intracranial EEG, on the detection of motor manifestations of epileptic seizures using surface electromyography (sEMG), accelerometry (ACM), video detection systems and mattress sensors and finally on the assessment of changes of physiologic parameters accompanying epileptic seizures measured by electrocardiography (ECG), respiratory monitors, pulse oximetry, surface temperature sensors, and electrodermal activity. Here we review automatic seizure detection based on scalp-EEG, ECG, and sEMG. Different seizure types affect preferentially different measurement parameters. While EEG changes accompany all types of seizures, sEMG and ACM are suitable mainly for detection of seizures with major motor manifestations. Therefore, seizure detection can be optimized by multimodal systems combining several measurement parameters. While most systems provide sensitivities over 70%, specificity expressed as false alarm rates still needs to be improved. Patients' acceptance and comfort of a specific device are of critical importance for its long-term application in a meaningful clinical way.

Seizure detection: do current devices work? And when can they be useful?

PURPOSE OF REVIEW

The unpredictability and apparent randomness of epileptic seizures is one of the most vexing aspects of epilepsy. Methods or devices capable of detecting seizures may help prevent injury or even death and significantly improve quality of life. Here, we summarize and evaluate currently available, unimodal, or polymodal detection systems for epileptic seizures, mainly in the ambulatory setting.

RECENT FINDINGS

There are two broad categories of detection devices: EEG-based and non-EEG-based systems. Wireless wearable EEG devices are now available both in research and commercial arenas. Neuro-stimulation devices are currently evolving and initial experiences of these show potential promise. As for non-EEG devices, different detecting systems show different sensitivity according to the different patient and seizure types. Regardless, when used in combination, these modalities may complement each other to increase positive predictive value. Although some devices with high sensitivity are promising, practical widespread use of such detection systems is still some way away. More research and experience are needed to evaluate the most efficient and integrated systems, to allow for better approaches to detection and prediction of seizures. The concept of closed-loop systems and prompt intervention may substantially improve quality of life for patients and carers.

Online detection of tonic-clonic seizures in pediatric patients using ECG and low-complexity incremental novelty detection

Home monitoring of refractory epilepsy patients has become of more interest the last couple of decades. A biomedical signal that can be used for online seizure detection at home is the electrocardiogram. Previous studies have shown that tonic-clonic seizures are most often accompanied with a strong heart rate increase. The main issue however is the strong patient-specific behavior of the ictal heart rate features, which makes it hard to make a patient-independent seizure detection algorithm. A patient-specific algorithm might be a solution, but existing methods require the availability of data of several seizures, which would make them inefficient in case the first seizure only occurs after a couple of days. Therefore an online method is described here that automatically converts from a patient-independent towards a patient-specific algorithm as more patient-specific data become available. This is done by using online feedback from the users to previously given alarms. By using a simplified one-class classifier, no seizure training data needs to be available for a good performance. The method is already able to adapt to the patient-specific characteristics after a couple of hours, and is able to detect 23 of 24 seizures longer than 10s, with an average of 0.38 false alarms per hour. Due to its low-complexity, it can be easily used for wearable seizure detection at home.

Automated real-time detection of tonic-clonic seizures using a wearable EMG device

Objective

To determine the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) using a wearable surface EMG device.

Methods

We prospectively tested the technical performance and diagnostic accuracy of real-time seizure detection using a wearable surface EMG device. The seizure detection algorithm and the cutoff values were prespecified. A total of 71 patients, referred to long-term video-EEG monitoring, on suspicion of GTCS, were recruited in 3 centers. Seizure detection was real-time and fully automated. The reference standard was the evaluation of video-EEG recordings by trained experts, who were blinded to data from the device. Reading the seizure logs from the device was done blinded to all other data.

Results

The mean recording time per patient was 53.18 hours. Total recording time was 3735.5 hours, and device deficiency time was 193 hours (4.9% of the total time the device was turned on). No adverse events occurred. The sensitivity of the wearable device was 93.8% (30 out of 32 GTCS were detected). Median seizure detection latency was 9 seconds (range −4 to 48 seconds). False alarm rate was 0.67/d.

Conclusions

The performance of the wearable EMG device fulfilled the requirements of patients: it detected GTCS with a sensitivity exceeding 90% and detection latency within 30 seconds.

Classification of evidence

This study provides Class II evidence that for people with a history of GTCS, a wearable EMG device accurately detects GTCS (sensitivity 93.8%, false alarm rate 0.67/d).

Devices for Ambulatory Monitoring of Sleep-Associated Disorders in Children with Neurological Diseases

Good sleep quality is essential for a child's wellbeing. Early sleep problems have been linked to the later development of emotional and behavioral disorders and can negatively impact the quality of life of the child and his or her family. Sleep-associated conditions are frequent in the pediatric population, and even more so in children with neurological problems. Monitoring devices can help to better characterize sleep efficiency and sleep quality. They can also be helpful to better characterize paroxysmal nocturnal events and differentiate between nocturnal seizures, parasomnias, and obstructive sleep apnea, each of which has a different management. Overnight ambulatory detection devices allow for a tolerable, low cost, objective assessment of sleep quality in the patient's natural environment. They can also be used as a notification system to allow for rapid recognition and prompt intervention of events like seizures. Optimal monitoring devices will be patient- and diagnosis-specific, but may include a combination of modalities such as ambulatory electroencephalograms, actigraphy, and pulse oximetry. We will summarize the current literature on ambulatory sleep devices for detecting sleep disorders in children with neurological diseases.

Multimodal, automated detection of nocturnal motor seizures at home: Is a reliable seizure detector feasible?

Objective

Automated seizure detection and alarming could improve quality of life and potentially prevent sudden, unexpected death in patients with severe epilepsy. As currently available systems focus on tonic–clonic seizures, we want to detect a broader range of seizure types, including tonic, hypermotor, and clusters of seizures.

Methods

In this multicenter, prospective cohort study, the nonelectroencephalographic (non‐EEG) signals heart rate and accelerometry were measured during the night in patients undergoing a diagnostic video‐EEG examination. Based on clinical video‐EEG data, seizures were classified and categorized as clinically urgent or not. Seizures included for analysis were tonic, tonic–clonic, hypermotor, and clusters of short myoclonic/tonic seizures. Features reflecting physiological changes in heart rate and movement were extracted. Detection algorithms were developed based on stepwise fulfillment of conditions during increases in either feature. A training set was used for development of algorithms, and an independent test set was used for assessing performance.

Results

Ninety‐five patients were included, but due to sensor failures, data from only 43 (of whom 23 patients had 86 seizures, representing 402 h of data) could be used for analysis. The algorithms yield acceptable sensitivities, especially for clinically urgent seizures (sensitivity = 71–87%), but produce high false alarm rates (2.3–5.7 per night, positive predictive value = 25–43%). There was a large variation in the number of false alarms per patient.

Significance

It seems feasible to develop a detector with high sensitivity, but false alarm rates are too high for use in clinical practice. For further optimization, personalization of algorithms may be necessary.

Online Automated Seizure Detection in Temporal Lobe Epilepsy Patients Using Single-lead ECG

Automated seizure detection in a home environment has been of increased interest the last couple of decades. The electrocardiogram is one of the signals that is suited for this application. In this paper, a new method is described that classifies different heart rate characteristics in order to detect seizures from temporal lobe epilepsy patients. The used support vector machine classifier is trained on data from other patients, so that the algorithm can be used directly from the start of each new recording. The algorithm was tested on a dataset of more than 918[Formula: see text]h of data coming from 17 patients containing 127 complex partial and generalized partial seizures. The algorithm was able to detect 81.89% of the seizures, with on average 1.97 false alarms per hour. These results show a strong drop in the number of false alarms of more than 50% compared to other heart rate-based patient-independent algorithms from the literature, at the expense of a slightly higher detection delay of 17.8s on average.

Detection of tonic epileptic seizures based on surface electromyography

The purpose of this project was to design an algorithm for detection of tonic seizures based on surface electromyography signals from the deltoids. A successful algorithm has a future prospect of being implemented in a wearable device as part of an alarm system. This has already been done for generalized tonic-clonic seizures, and the hypothesis was that some of the same characteristics could be found for tonic seizures. The signals were pre-processed by a high-pass filter to remove low frequency noise such as movement artifacts. Several different features were investigated, including kurtosis, median frequency, zero crossing rate and approximate entropy. These features were used as input in the random forest classifier to decide if a data segment was from a seizure or not. The goal was to develop a generic algorithm for all tonic seizures, but better results were achieved when certain parameters were adapted specifically for each patient. With patient specific parameters the algorithm obtained a sensitivity of 100% for four of six patients with false detection rates between 0.08 and 7.90 per hour.

Feature selection methods for accelerometry-based seizure detection in children

We investigate the application of feature selection methods and their influence on distinguishing nocturnal motor seizures in epileptic children from normal nocturnal movements using accelerometry signals. We studied two feature selection methods applied one after the other to reduce the complexity and computation costs of least-squares support vector machine (LS-SVM) models. Simultaneous feature selection analyses were performed for each seizure type individually and jointly. Starting from 140 features, a filter method based on mutual information was applied to remove irrelevant and redundant features. The obtained subset was further reduced through a wrapper feature selection strategy using an LS-SVM classifier with both forward search and backward elimination. The discriminative power of each feature subset was evaluated on the test data in terms of the area under the receiver operating characteristic curve, sensitivity, and false detection rate per hour. We showed that, by using only a filter method for feature selection, it was possible to obtain classification results of comparable or slightly reduced performance with respect to the complete feature set. The attained results could facilitate further development of accelerometry-based seizure detection and alarm systems.

Non-EEG based ambulatory seizure detection designed for home use: What is available and how will it influence epilepsy care?

OBJECTIVE

This study aimed to (1) evaluate available systems and algorithms for ambulatory automatic seizure detection and (2) discuss benefits and disadvantages of seizure detection in epilepsy care.

METHODS

PubMed and EMBASE were searched up to November 2014, using variations and synonyms of search terms "seizure prediction" OR "seizure detection" OR "seizures" AND "alarm".

RESULTS

Seventeen studies evaluated performance of devices and algorithms to detect seizures in a clinical setting. Algorithms detecting generalized tonic-clonic seizures (GTCSs) had varying sensitivities (11% to 100%) and false alarm rates (0.2-4/24 h). For other seizure types, detection rates were low, or devices produced many false alarms. Five studies externally validated the performance of four different devices for the detection of GTCSs. Two devices were promising in both children and adults: a mattress-based nocturnal seizure detector (sensitivity: 84.6% and 100%; false alarm rate: not reported) and a wrist-based detector (sensitivity: 89.7%; false alarm rate: 0.2/24 h).

SIGNIFICANCE

Detection of seizure types other than GTCSs is currently unreliable. Two detection devices for GTCSs provided promising results when externally validated in a clinical setting. However, these devices need to be evaluated in the home setting in order to establish their true value. Automatic seizure detection may help prevent sudden unexpected death in epilepsy or status epilepticus, provided the alarm is followed by an effective intervention. Accurate seizure detection may improve the quality of life (QoL) of subjects and caregivers by decreasing burden of seizure monitoring and may facilitate diagnostic monitoring in the home setting. Possible risks are occurrence of alarm fatigue and invasion of privacy. Moreover, an unexpectedly high seizure frequency might be detected for which there are no treatment options. We propose that future studies monitor benefits and disadvantages of seizure detection systems with particular emphasis on QoL, comfort, and privacy of subjects and impact of false alarms.

Using wearable sensors for semiology-independent seizure detection - towards ambulatory monitoring of epilepsy

Epilepsy is a disease of the central nervous system. Nearly 70% of people with epilepsy respond to a proper treatment, but for a successful therapy of epilepsy, physicians need to know if and when seizures occur. The gold standard diagnosis tool video-electroencephalography (vEEG) requires patients to stay at hospital for several days. A wearable sensor system, e.g. a wristband, serving as diagnostic tool or event monitor, would allow unobtrusive ambulatory long-term monitoring while reducing costs. Previous studies showed that seizures with motor symptoms such as generalized tonic-clonic seizures can be detected by measuring the electrodermal activity (EDA) and motion measuring acceleration (ACC). In this study, EDA and ACC from 8 patients were analyzed. In extension to previous studies, different types of seizures, including seizures without motor activity, were taken into account. A hierarchical classification approach was implemented in order to detect different types of epileptic seizures using data from wearable sensors. Using a k-nearest neighbor (kNN) classifier an overall sensitivity of 89.1% and an overall specificity of 93.1% were achieved, for seizures without motor activity the sensitivity was 97.1% and the specificity was 92.9%. The presented method is a first step towards a reliable ambulatory monitoring system for epileptic seizures with and without motor activity.

Automated Detection of Tonic-Clonic Seizures Using 3-D Accelerometry and Surface Electromyography in Pediatric Patients

Epileptic seizure detection is traditionally done using video/electroencephalography monitoring, which is not applicable for long-term home monitoring. In recent years, attempts have been made to detect the seizures using other modalities. In this study, we investigated the application of four accelerometers (ACM) attached to the limbs and surface electromyography (sEMG) electrodes attached to upper arms for the detection of tonic-clonic seizures. sEMG can identify the tension during the tonic phase of tonic-clonic seizure, while ACM is able to detect rhythmic patterns of the clonic phase of tonic-clonic seizures. Machine learning techniques, including feature selection and least-squares support vector machine classification, were employed for detection of tonic-clonic seizures from ACM and sEMG signals. In addition, the outputs of ACM and sEMG-based classifiers were combined using a late integration approach. The algorithms were evaluated on 1998.3 h of data recorded nocturnally in 56 patients of which seven had 22 tonic-clonic seizures. A multimodal approach resulted in a more robust detection of short and nonstereotypical seizures (91%), while the number of false alarms increased significantly compared with the use of single sEMG modality (0.28-0.5/12h). This study also showed that the choice of the recording system should be made depending on the prevailing pediatric patient-specific seizure characteristics and nonepileptic behavior.

Automatic Detection and Classification of Convulsive Psychogenic Nonepileptic Seizures Using a Wearable Device

Epilepsy is one of the most common neurological disorders and patients suffer from unprovoked seizures. In contrast, psychogenic nonepileptic seizures (PNES) are another class of seizures that are involuntary events not caused by abnormal electrical discharges but are a manifestation of psychological distress. The similarity of these two types of seizures poses diagnostic challenges that often leads in delayed diagnosis of PNES. Further, the diagnosis of PNES involves high-cost hospital admission and monitoring using video-electroencephalogram machines. A wearable device that can monitor the patient in natural setting is a desired solution for diagnosis of convulsive PNES. A wearable device with an accelerometer sensor is proposed as a new solution in the detection and diagnosis of PNES. The seizure detection algorithm and PNES classification algorithm are developed. The developed algorithms are tested on data collected from convulsive epileptic patients. A very high seizure detection rate is achieved with 100% sensitivity and few false alarms. A leave-one-out error of 6.67% is achieved in PNES classification, demonstrating the usefulness of wearable device in the diagnosis of PNES.

Automated differentiation between epileptic and nonepileptic convulsive seizures

Our objective was the clinical validation of an automated algorithm based on surface electromyography (EMG) for differentiation between convulsive epileptic and psychogenic nonepileptic seizures (PNESs). Forty-four consecutive episodes with convulsive events were automatically analyzed with the algorithm: 25 generalized tonic-clonic seizures (GTCSs) from 11 patients, and 19 episodes of convulsive PNES from 13 patients. The gold standard was the interpretation of the video-electroencephalographic recordings by experts blinded to the EMG results. The algorithm correctly classified 24 GTCSs (96%) and 18 PNESs (95%). The overall diagnostic accuracy was 95%. This algorithm is useful for distinguishing between epileptic and psychogenic convulsive seizures.