目的:本研究旨在开发和评估一种基于机器学习的算法,用于使用新型多模态连接衬衫检测局灶性至双侧强直阵挛性癫痫发作(FBTCS)。
方法:我们前瞻性地招募了癫痫患者入住我们的癫痫监测单元,并要求他们在同时进行视频脑电图监测时穿上连接的衬衫。使用连接的衬衫记录的心电图(ECG)和加速度(ACC)信号用于开发癫痫发作检测算法。首先,我们使用滑动窗口从ECG和ACC信号中提取线性和非线性特征。然后,我们训练了一种极端梯度增强算法(XGBoost),根据由三名董事会认证的癫痫学家注释的癫痫发作和偏移来检测FBTCS.最后,我们应用了后处理步骤来正则化分类输出。实施了耐心嵌套交叉验证,以评估灵敏度方面的性能,误报率(FAR),错误警告时间(TiW),检测延迟,和受试者工作特征曲线下面积(ROC-AUC)。
结果:我们记录了42名患者的66个FBTCS,这些患者穿着连接的衬衫,总共连续8067小时。XGBoost算法的灵敏度达到84.8%(56/66发作),FAR中位数为.55/24小时,TiW中位数为10秒/报警。ROC-AUC为.90(95%置信区间=.88-.91)。从进展到双侧强直阵挛性阶段的中位检测潜伏期为25.5s。
结论:新型连接衬衫允许在医院环境中以低误报率准确检测FBTCS。需要在具有实时和在线癫痫发作检测算法的住宅环境中进行前瞻性研究,以验证该设备的性能和可用性。
OBJECTIVE: This study was undertaken to develop and evaluate a machine learning-based algorithm for the detection of focal to bilateral tonic-clonic seizures (FBTCS) using a novel multimodal connected shirt.
METHODS: We prospectively recruited patients with epilepsy admitted to our epilepsy monitoring unit and asked them to wear the connected shirt while under simultaneous video-electroencephalographic monitoring. Electrocardiographic (ECG) and accelerometric (ACC) signals recorded with the connected shirt were used for the development of the seizure detection algorithm. First, we used a sliding window to extract linear and nonlinear features from both ECG and ACC signals. Then, we trained an extreme gradient boosting algorithm (XGBoost) to detect FBTCS according to seizure onset and offset annotated by three board-certified epileptologists. Finally, we applied a postprocessing step to regularize the classification output. A patientwise nested cross-validation was implemented to evaluate the performances in terms of sensitivity, false alarm rate (FAR), time in false warning (TiW), detection latency, and receiver operating characteristic area under the curve (ROC-AUC).
RESULTS: We recorded 66 FBTCS from 42 patients who wore the connected shirt for a total of 8067 continuous hours. The XGBoost algorithm reached a sensitivity of 84.8% (56/66 seizures), with a median FAR of .55/24 h and a median TiW of 10 s/alarm. ROC-AUC was .90 (95% confidence interval = .88-.91). Median detection latency from the time of progression to the bilateral tonic-clonic phase was 25.5 s.
CONCLUSIONS: The novel connected shirt allowed accurate detection of FBTCS with a low false alarm rate in a hospital setting. Prospective studies in a residential setting with a real-time and online seizure detection algorithm are required to validate the performance and usability of this device.