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Published on: May 5, 2020
[Automatic detection model of hypertrophic cardiomyopathy based on deep convolutional neural network]
Yuxiang Bu1, Xingzeng Cha1, Jinling Zhu1
1School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China.
Insights
This study introduces a deep learning approach using convolution neural networks (CNNs) for automatic hypertrophic cardiomyopathy (HCM) detection from single-lead electrocardiogram (ECG) signals. The method achieved high accuracy, aiding in early risk assessment and large-scale screening.
Area of Science:
- Cardiology
- Biomedical Engineering
- Artificial Intelligence in Medicine
Background:
- Hypertrophic cardiomyopathy (HCM) diagnosis is crucial for sudden cardiac death risk stratification and genetic screening.
- Conventional multi-lead ECG methods have technical limitations.
- Early and accurate detection of HCM is essential for patient management.
Purpose of the Study:
- To develop an automated method for hypertrophic cardiomyopathy (HCM) detection using single-lead electrocardiogram (ECG) signals.
- To leverage deep learning, specifically convolution neural networks (CNNs), for enhanced ECG analysis.
- To provide a tool for efficient, large-scale preliminary screening of HCM.
Main Methods:
- Utilized single-lead ECG signals as input data.
- Implemented R-wave peak detection, signal segmentation, and resampling per heartbeat.
- Developed and optimized a convolution neural network (CNN) model for feature extraction and HCM classification.
- Trained the model on a dataset of 14,459 heartbeats from public PhysioNet databases.
Main Results:
- The optimized CNN model demonstrated high performance in detecting HCM.
- Achieved an accuracy of 95.98%, sensitivity of 98.03%, and specificity of 95.79%.
- The deep learning approach effectively analyzed single-lead ECG data.
Conclusions:
- Deep learning, specifically CNNs, offers an effective method for automated HCM detection from single-lead ECG.
- This approach overcomes limitations of traditional multi-lead ECG methods.
- The developed method has significant potential for assisting clinicians in rapid, large-scale HCM screening.
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.
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