Integrating multi-domain deep features of electrocardiogram and phonocardiogram for coronary artery disease detection

心音图 计算机辅助设计 计算机科学 卷积神经网络 人工智能 深度学习 冠状动脉疾病 特征(语言学) 特征提取 模式识别(心理学) 心脏病学 灵敏度(控制系统) 频域 医学 计算机视觉 工程类 哲学 语言学 工程制图 电子工程
作者
Han Li,Xinpei Wang,Changchun Liu,Peng Li,Yu Jiao
出处
期刊:Computers in Biology and Medicine [Elsevier BV]
卷期号:138: 104914-104914 被引量:35
标识
DOI:10.1016/j.compbiomed.2021.104914
摘要

Electrocardiogram (ECG) and phonocardiogram (PCG) are both noninvasive and convenient tools that can capture abnormal heart states caused by coronary artery disease (CAD). However, it is very challenging to detect CAD relying on ECG or PCG alone due to low diagnostic sensitivity. Recently, several studies have attempted to combine ECG and PCG signals for diagnosing heart abnormalities, but only conventional manual features have been used. Considering the strong feature extraction capabilities of deep learning, this paper develops a multi-input convolutional neural network (CNN) framework that integrates time, frequency, and time-frequency domain deep features of ECG and PCG for CAD detection. Simultaneously recorded ECG and PCG signals from 195 subjects are used. The proposed framework consists of 1-D and 2-D CNN models and uses signals, spectrum images, and time-frequency images of ECG and PCG as inputs. The framework combining multi-domain deep features of two-modal signals is very effective in classifying non-CAD and CAD subjects, achieving an accuracy, sensitivity, and specificity of 96.51%, 99.37%, and 90.08%, respectively. The comparison with existing studies demonstrates that our method is very competitive in CAD detection. The proposed approach is very promising in assisting the real-world CAD diagnosis, especially under general medical conditions.
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