应用遗传算法优化神经网络的致死性心电节律辨识算法研究  

Research on malignant arrhythmia detection algorithm using neural network optimized by genetic algorithm

作　　者：余明[1] 陈锋[1] 张广[1] 李良喆 王春晨[1] 詹宁波[1] 顾彪[1] 韦婧[1] 吴太虎[1] 

机构地区：[1]军事医学科学院卫生装备研究所,天津300161

出　　处：《生物医学工程学杂志》2017年第3期421-430,共10页Journal of Biomedical Engineering

基　　金：国家自然科学基金资助项目(81501551)

摘　　要：致死性心电节律的辨识和分类是自动体外除颤仪的关键任务。本文对已存在的心电节律辨识算法提取出的21个特征值进行了回顾性研究,并基于这些特征值构建了一个遗传算法优化的反向传播神经网络。以数据库提供的1 343例心电信号样本用于实验。实验结果表明,本文构建的神经网络在对窦性节律、心室颤动、室性心动过速、心脏停搏4类心电信号的辨识分类上有很好的表现,在测试集上的平衡准确性高达99.06%;相较已存在的算法,辨识性能更好。将该算法应用在自动体外除颤仪上,将进一步提高除颤前节律分析的可靠性,最终提高心脏骤停的存活率。Detection and classification of malignant arrhythmia are key tasks of automated external defibrillators. In this paper, 21 metrics extracted from existing algorithms were studied by retrospective analysis. Based on these metrics, a back propagation neural network optimized by genetic algorithm was constructed. A total of 1,343 electrocardiogram samples were included in the analysis. The results of the experiments indicated that this network had a good performance in classification of sinus rhythm, ventricular fibrillation, ventricular tachycardia and asystole. The balanced accuracy on test dataset reached up to 99.06%. It illustrates that our proposed detection algorithm is obviously superior to existing algorithms. The application of the algorithm in the automated external defibrillators will further improve the reliability of rhythm analysis before defibrillation and ultimately improve the survival rate of cardiac arrest.

关 键 词：心脏骤停 自动体外除颤仪 遗传算法 反向传播神经网络 

分 类 号：R541.7[医药卫生—心血管疾病] TP18[医药卫生—内科学]