Zhang Qiang, Chen Xianxiang, Fang Zhen, Zhan Qingyuan, Yang Ting, Xia Shanhong
Institute of Electronics, Chinese Academy of Sciences, Beijing, People's Republic of China. University of Chinese Academy of Sciences, Beijing, People's Republic of China.
Physiol Meas. 2017 Feb;38(2):259-271. doi: 10.1088/1361-6579/38/2/259. Epub 2017 Jan 18.
To lessen the rate of false critical arrhythmia alarms, we used robust heart rate estimation and cost-sensitive support vector machines. The PhysioNet MIMIC II database and the 2015 PhysioNet/CinC Challenge public database were used as the training dataset; the 2015 Challenge hidden dataset was for testing. Each record had an alarm labeled with asystole, extreme bradycardia, extreme tachycardia, ventricular tachycardia or ventricular flutter/fibrillation. Before alarm onsets, 300 s multimodal data was provided, including electrocardiogram, arterial blood pressure and/or photoplethysmogram. A signal quality modified Kalman filter achieved robust heart rate estimation. Based on this, we extracted heart rate variability features and statistical ECG features. Next, we applied a genetic algorithm (GA) to select the optimal feature combination. Finally, considering the high cost of classifying a true arrhythmia as false, we selected cost-sensitive support vector machines (CSSVMs) to classify alarms. Evaluation on the test dataset showed the overall true positive rate was 95%, and the true negative rate was 85%.
为了降低错误的危急心律失常警报率，我们使用了稳健的心率估计和成本敏感型支持向量机。将PhysioNet MIMIC II数据库和2015年PhysioNet/CinC挑战赛公共数据库用作训练数据集；2015年挑战赛隐藏数据集用于测试。每条记录都有一个标记为心脏停搏、极度心动过缓、极度心动过速、室性心动过速或室颤/室扑的警报。在警报发作前，提供300秒的多模态数据，包括心电图、动脉血压和/或光电容积脉搏波。一种信号质量改进的卡尔曼滤波器实现了稳健的心率估计。基于此，我们提取了心率变异性特征和统计心电图特征。接下来，我们应用遗传算法（GA）选择最优特征组合。最后，考虑到将真正的心律失常误分类的成本很高，我们选择成本敏感型支持向量机（CSSVM）对警报进行分类。在测试数据集上的评估表明，总体真阳性率为95%，真阴性率为85%。
