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

IF 2.7 3区 医学 Q2 CLINICAL NEUROLOGY
Guangming Wang , Hao Yan , Wen Li , Duozheng Sheng , Liankun Ren , Qun Wang , Hua Zhang , Guojun Zhang , Tao Yu , Gang Wang
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引用次数: 0

Abstract

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.
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来源期刊
Seizure-European Journal of Epilepsy
Seizure-European Journal of Epilepsy 医学-临床神经学
CiteScore
5.60
自引率
6.70%
发文量
231
审稿时长
34 days
期刊介绍: Seizure - European Journal of Epilepsy is an international journal owned by Epilepsy Action (the largest member led epilepsy organisation in the UK). It provides a forum for papers on all topics related to epilepsy and seizure disorders.
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