基于深度学习和模糊C均值的心电信号分类方法

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.

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

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.