Thayer School of Engineering, Dartmouth College, United States.
Geisel School of Medicine, Dartmouth College, Thayer School of Engineering, Dartmouth College (adjunct Appointment); and Dartmouth-Hitchcock Medical Center, United States.
Comput Biol Med. 2021 Mar;130:104232. doi: 10.1016/j.compbiomed.2021.104232. Epub 2021 Jan 21.
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.
本文探讨了使用非脑部、时间序列数据来检测癫痫发作的可行性。数据来自十五名患者（7 名男性，5 名女性，3 名未注明，平均年龄 36.17 岁），其中五名患者共发生了七次癫痫发作。患者在住院环境中使用标准视频脑电图（vEEG）进行监测，同时还佩戴了监测心电图、皮肤电活动、肌电图、加速度和音频信号（发声）的传感器。我们进行了一项系统而详细的研究，以确定对分离癫痫发作期间和非癫痫发作期间获得的数据最有贡献的传感器和源自非脑部传感器的特征。使用线性判别分析（LDA）对数据进行后处理表明，基于从记录信号中得出的特征，癫痫发作数据与非癫痫发作数据具有很强的可分离性。使用 LDA 计算的每位经历过癫痫发作的患者的接收者操作特性（ROC）曲线下的平均面积为 0.9682。对每个患者最有贡献的特征检测到癫痫发作的特征因患者而异。结果表明，使用指定传感器套件的多模态癫痫检测方法在检测癫痫发作方面具有很高的灵敏度和特异性。此外，该研究还提供了一种量化每个传感器和特征对可分离性贡献的方法。开发一种基于非脑电图（EEG）的癫痫检测设备将使医生在临床环境之外更准确地计算癫痫发作次数，从而改善癫痫患者的治疗和生活质量。
