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

Kurada, Abhinav V.; Srinivasan, Tarun; Hammond, Sarah P.; Ulate‐Campos, Adriana; Bidwell, Jonathan

doi:10.1016/j.seizure.2019.02.007

Cited by 30 publications

(21 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…False alarms were categorized as (1) awake and playing or moving in bed, (2) rhythmic movement disorder (eg, body rocking), (3) rhythmically moving object in the room, and (4) another person in the room. For the generalizability of the results, we also calculated the F 1 score for the detection of convulsive seizures 9 …”

Section: Methodsmentioning

confidence: 99%

Automated video‐based detection of nocturnal motor seizures in children

et al. 2020

View full text Add to dashboard Cite

Seizure detection devices can improve epilepsy care, but wearables are not always tolerated. We previously demonstrated good performance of a real‐time video‐based algorithm for detection of nocturnal convulsive seizures in adults with learning disabilities. The algorithm calculates the relative frequency content based on the group velocity reconstruction from video‐sequence optical flow. We aim to validate the video algorithm on nocturnal motor seizures in a pediatric population. We retrospectively analyzed the algorithm performance on a database including 1661 full recorded nights of 22 children (age = 3‐17 years) with refractory epilepsy at home or in a residential care setting. The algorithm detected 118 of 125 convulsions (median sensitivity per participant = 100%, overall sensitivity = 94%, 95% confidence interval = 61%‐100%) and identified all 135 hyperkinetic seizures. Most children had no false alarms; 81 false alarms occurred in six children (median false alarm rate [FAR] per participant per night = 0 [range = 0‐0.47], overall FAR = 0.05 per night). Most false alarms (62%) were behavior‐related (eg, awake and playing in bed). Our noncontact detection algorithm reliably detects nocturnal epileptic events with only a limited number of false alarms and is suitable for real‐time use.

show abstract

Section: Methodsmentioning

confidence: 99%

Automated video‐based detection of nocturnal motor seizures in children

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Seizure detection devices and their performances were previously reviewed by several groups 13,14 . In contrast to our study, most studies aimed at the detection of seizures with major motor symptoms including generalized convulsive seizures (ie, focal to bilateral tonic‐clonic seizures or generalized tonic‐clonic seizures) and therefore mostly used devices targeting movements of extremities or electric muscle activity.…”

Section: Discussionmentioning

confidence: 99%

Performance of ECG‐based seizure detection algorithms strongly depends on training and test conditions

et al. 2021

View full text Add to dashboard Cite

Objective To identify non‐EEG‐based signals and algorithms for detection of motor and non‐motor seizures in people lying in bed during video‐EEG (VEEG) monitoring and to test whether these algorithms work in freely moving people during mobile EEG recordings. Methods Data of three groups of adult people with epilepsy (PwE) were analyzed. Group 1 underwent VEEG with additional devices (accelerometry, ECG, electrodermal activity); group 2 underwent VEEG; and group 3 underwent mobile EEG recordings both including one‐lead ECG. All seizure types were analyzed. Feature extraction and machine‐learning techniques were applied to develop seizure detection algorithms. Performance was expressed as sensitivity, precision, F1 score, and false positives per 24 hours. Results The algorithms were developed in group 1 (35 PwE, 33 seizures) and achieved best results (F1 score 56%, sensitivity 67%, precision 45%, false positives 0.7/24 hours) when ECG features alone were used, with no improvement by including accelerometry and electrodermal activity. In group 2 (97 PwE, 255 seizures), this ECG‐based algorithm largely achieved the same performance (F1 score 51%, sensitivity 39%, precision 73%, false positives 0.4/24 hours). In group 3 (30 PwE, 51 seizures), the same ECG‐based algorithm failed to meet up with the performance in groups 1 and 2 (F1 score 27%, sensitivity 31%, precision 23%, false positives 1.2/24 hours). ECG‐based algorithms were also separately trained on data of groups 2 and 3 and tested on the data of the other groups, yielding maximal F1 scores between 8% and 26%. Significance Our results suggest that algorithms based on ECG features alone can provide clinically meaningful performance for automatic detection of all seizure types. Our study also underscores that the circumstances under which such algorithms were developed, and the selection of the training and test data sets need to be considered and limit the application of such systems to unseen patient groups behaving in different conditions.

show abstract

“…Such devices may be helpful in the future for medication trials that need to evaluate seizure frequency accurately. 20 However, current literature suggests an underestimation exists in such biometric parameters such as heart rate in similar devices, so more validation for use of such devices in seizure monitoring is needed. 11,21…”

Section: Discussionmentioning

confidence: 99%

Evaluation of an Activity Tracker to Detect Seizures Using Machine Learning

et al. 2020

View full text Add to dashboard Cite

Currently, the tracking of seizures is highly subjective, dependent on qualitative information provided by the patient and family instead of quantifiable seizure data. Usage of a seizure detection device to potentially detect seizure events in a population of epilepsy patients has been previously done. Therefore, we chose the Fitbit Charge 2 smart watch to determine if it could detect seizure events in patients when compared to continuous electroencephalographic (EEG) monitoring for those admitted to an epilepsy monitoring unit. A total of 40 patients were enrolled in the study that met the criteria between 2015 and 2016. All seizure types were recorded. Twelve patients had a total of 53 epileptic seizures. The patient-aggregated receiver operating characteristic curve had an area under the curve of 0.58 [0.56, 0.60], indicating that the neural network models were generally able to detect seizure events at an above-chance level. However, the overall low specificity implied a false alarm rate that would likely make the model unsuitable in practice. Overall, the use of the Fitbit Charge 2 activity tracker does not appear well suited in its current form to detect epileptic seizures in patients with seizure activity when compared to data recorded from the continuous EEG.

show abstract

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

Cited by 30 publications

References 66 publications

Automated video‐based detection of nocturnal motor seizures in children

Automated video‐based detection of nocturnal motor seizures in children

Performance of ECG‐based seizure detection algorithms strongly depends on training and test conditions

Evaluation of an Activity Tracker to Detect Seizures Using Machine Learning

Contact Info

Product

Resources

About