光容积图
可穿戴计算机
癫痫
心电图
计算机科学
心率
可穿戴技术
癫痫发作
脑电图
医学
人工智能
计算机视觉
心脏病学
内科学
嵌入式系统
血压
滤波器(信号处理)
精神科
作者
Kaat Vandecasteele,Thomas De Cooman,Ying Gu,Evy Cleeren,Kasper Claes,Wim Van Paesschen,Sabine Van Huffel,Borbála Hunyadi
出处
期刊:Sensors
[Multidisciplinary Digital Publishing Institute]
日期:2017-10-13
卷期号:17 (10): 2338-2338
被引量:162
摘要
Electrocardiography has added value to automatically detect seizures in temporal lobe epilepsy (TLE) patients. The wired hospital system is not suited for a long-term seizure detection system at home. To address this need, the performance of two wearable devices, based on electrocardiography (ECG) and photoplethysmography (PPG), are compared with hospital ECG using an existing seizure detection algorithm. This algorithm classifies the seizures on the basis of heart rate features, extracted from the heart rate increase. The algorithm was applied to recordings of 11 patients in a hospital setting with 701 h capturing 47 (fronto-)temporal lobe seizures. The sensitivities of the hospital system, the wearable ECG device and the wearable PPG device were respectively 57%, 70% and 32%, with corresponding false alarms per hour of 1.92, 2.11 and 1.80. Whereas seizure detection performance using the wrist-worn PPG device was considerably lower, the performance using the wearable ECG is proven to be similar to that of the hospital ECG.
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