Multimodal wearable EEG, EMG and accelerometry measurements improve the accuracy of tonic-clonic seizure detection.

IF 2.3 4区 医学 Q3 BIOPHYSICS Physiological measurement Pub Date : 2024-06-07 DOI:10.1088/1361-6579/ad4e94
Jingwei Zhang, Lauren Swinnen, Christos Chatzichristos, Victoria Broux, Renee Proost, Katrien Jansen, Benno Mahler, Nicolas Zabler, Nino Epitashvilli, Matthias Dümpelmann, Andreas Schulze-Bonhage, Elisabeth Schriewer, Ummahan Ermis, Stefan Wolking, Florian Linke, Yvonne Weber, Mkael Symmonds, Arjune Sen, Andrea Biondi, Mark P Richardson, Abuhaiba Sulaiman I, Ana Isabel Silva, Francisco Sales, Gergely Vértes, Wim Van Paesschen, Maarten De Vos
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Abstract

Objective. This paper aims to investigate the possibility of detecting tonic-clonic seizures (TCSs) with behind-the-ear, two-channel wearable electroencephalography (EEG), and to evaluate its added value to non-EEG modalities in TCS detection.Methods. We included 27 participants with a total of 44 TCSs from the European multicenter study SeizeIT2. The wearable Sensor Dot (Byteflies) was used to measure behind-the-ear EEG, electromyography (EMG), electrocardiography, accelerometry (ACC) and gyroscope. We evaluated automatic unimodal detection of TCSs, using sensitivity, precision, false positive rate (FPR) and F1-score. Subsequently, we fused the different modalities and again assessed performance. Algorithm-labeled segments were then provided to two experts, who annotated true positive TCSs, and discarded false positives.Results. Wearable EEG outperformed the other single modalities with a sensitivity of 100% and a FPR of 10.3/24 h. The combination of wearable EEG and EMG proved most clinically useful, delivering a sensitivity of 97.7%, an FPR of 0.4/24 h, a precision of 43%, and an F1-score of 59.7%. The highest overall performance was achieved through the fusion of wearable EEG, EMG, and ACC, yielding a sensitivity of 90.9%, an FPR of 0.1/24 h, a precision of 75.5%, and an F1-score of 82.5%.Conclusions. In TCS detection with a wearable device, combining EEG with EMG, ACC or both resulted in a remarkable reduction of FPR, while retaining a high sensitivity.Significance. Adding wearable EEG could further improve TCS detection, relative to extracerebral-based systems.

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多模态可穿戴脑电图、肌电图和加速度测量提高了强直阵挛发作检测的准确性。
目的:本文旨在研究耳后双通道可穿戴脑电图(EEG)检测强直阵挛发作(TCS)的可能性,并评估其在 TCS 检测中与非 EEG 模式相比的附加值:我们纳入了欧洲多中心研究 SeizeIT2 的 27 名参与者,他们共患有 44 种 TCS。可穿戴传感器 Dot(SD;Byteflies)用于测量耳后脑电图(EEG)、肌电图(EMG)、心电图(ECG)、加速度计(ACC)和陀螺仪(GYR)。我们使用灵敏度、精确度、假阳性率 (FPR) 和 F1 分数对 TCS 的自动单模态检测进行了评估。随后,我们融合了不同的模式,并再次评估了性能。然后,将算法标记的片段提供给两位专家,由他们注释真正的阳性 TCS,并剔除假阳性 (FP):结果:可穿戴脑电图的灵敏度为 100%,FPR 为 10.3/24h,优于其他单一模式。可穿戴脑电图和肌电图的组合被证明在临床上最有用,灵敏度为 97.7%,FPR 为 0.4/24h,精确度为 43%,F1 分数为 59.7%。融合可穿戴脑电图、肌电图和 ACC 的总体性能最高,灵敏度为 90.9%,FPR 为 0.1/24h,精确度为 75.5%,F1 分数为 82.5%:结论:在使用可穿戴设备进行 TCS 检测时,将脑电图与肌电图、ACC 或两者相结合可显著降低 FPR,同时保持较高的灵敏度:与基于脑外的系统相比,增加可穿戴脑电图可进一步改善 TCS 检测。
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来源期刊
Physiological measurement
Physiological measurement 生物-工程:生物医学
CiteScore
5.50
自引率
9.40%
发文量
124
审稿时长
3 months
期刊介绍: Physiological Measurement publishes papers about the quantitative assessment and visualization of physiological function in clinical research and practice, with an emphasis on the development of new methods of measurement and their validation. Papers are published on topics including: applied physiology in illness and health electrical bioimpedance, optical and acoustic measurement techniques advanced methods of time series and other data analysis biomedical and clinical engineering in-patient and ambulatory monitoring point-of-care technologies novel clinical measurements of cardiovascular, neurological, and musculoskeletal systems. measurements in molecular, cellular and organ physiology and electrophysiology physiological modeling and simulation novel biomedical sensors, instruments, devices and systems measurement standards and guidelines.
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