基于深度学习和模糊C均值的心电信号分类方法  

A Method for ECG Classification Using Deep Learning and Fuzzy C-means

作　　者：吴志勇[1,2] 丁香乾[1] 许晓伟[1] 鞠传香[2] WU Zhi-Yong;DING Xiang-Qian;XU Xiao-Wei;JU Chuan-Xiang(College of Information Science and Engineering,Ocean University of China,Qingdao 266100;School of Computer Science and Technology,Shandong University of Technology,Zibo 255000)

机构地区：[1]中国海洋大学信息科学与工程学院,青岛266100 [2]山东理工大学计算机科学与技术学院,淄博255000

出　　处：《自动化学报》2018年第10期1913-1920,共8页Acta Automatica Sinica

基　　金：国家重点研发计划(2016YFB1001103)资助~~

摘　　要：针对长时海量心电信号自动分类系统中,心电专家诊断费时、费力和成本高,心电信号形态复杂导致特征提取困难,异常诊断模型适应性差、准确度低等问题,本文提出一种基于深度学习和模糊C均值的心电信号分类方法.该方法主要包括心电信号降噪预处理、心电信号分段和采样点统一化、无监督心跳特征学习、模糊C均值分类4个步骤,给出了模糊C均值深度信念网络FCMDBN模型结构和学习分类算法.仿真实验基于MIT-BIH心率异常数据库表明,与基于传统心电特征人工设计的分类方法相比,本文提出的信号诊断方法具有较高的适应性和准确度.In the classification system for longtime and massive ECG signals, ECG diagnosis is time-consuming, laborious and costly. It is difficult to extract signal features because of the complex ECG morphology. The diagnosis model has low adaptability and accuracy. To solve the above problem, a novel method for ECG classification using deep learning and fuzzy C-means is proposed. The method includes four steps： ECG signal preprocessing, heartbeat segmentation and sampling point unification,ECG feature deep learning, fuzzy C-means classification. The structure and algorithm of fuzzy C-means deep belief networks（FCMDBN） are shown in the paper. The method is validated on the well-known MIT-BIH arrhythmia database. Experiment results show that the approach achieves higher adaptability and accuracy than traditional hand-designed methods on classification of ECG signals.

关 键 词：心电信号分类 深度学习 模糊C均值 深度信念网络 

分 类 号：TN911.7[电子电信—通信与信息系统] R540.4[电子电信—信息与通信工程]