[Automatic detection model of hypertrophic cardiomyopathy based on deep convolutional neural network]

Abstract

The diagnosis of hypertrophic cardiomyopathy (HCM) is of great significance for the early risk classification of sudden cardiac death and the screening of family genetic diseases. This research proposed a HCM automatic detection method based on convolution neural network (CNN) model, using single-lead electrocardiogram (ECG) signal as the research object. Firstly, the R-wave peak locations of single-lead ECG signal were determined, followed by the ECG signal segmentation and resample in units of heart beats, then a CNN model was built to automatically extract the deep features in the ECG signal and perform automatic classification and HCM detection. The experimental data is derived from 108 ECG records extracted from three public databases provided by PhysioNet, the database established in this research consists of 14,459 heartbeats, and each heartbeat contains 128 sampling points. The results revealed that the optimized CNN model could effectively detect HCM, the accuracy, sensitivity and specificity were 95.98%, 98.03% and 95.79% respectively. In this research, the deep learning method was introduced for the analysis of single-lead ECG of HCM patients, which could not only overcome the technical limitations of conventional detection methods based on multi-lead ECG, but also has important application value for assisting doctor in fast and convenient large-scale HCM preliminary screening.

Keywords: Convolutional neural network; Deep learning; Hypertrophic cardiomyopathy; Single-lead electrocardiogram.

图 1

Schematic diagram of the proposed HCM automatic detection algorithm based on convolution neural network (CNN) by using single-lead electrocardiogram (ECG) signal 基于单导联ECG的HCM自动检测算法的方法流程框图

图 2

Demonstration of database building 本文数据库建库样本示例

图 3

Structure and parameters of the CNN model after optimization 模型优化后的卷积神经网络结构及参数