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Published on: December 15, 2023
Arrhythmia detection using deep convolutional neural network with long duration ECG signals
Özal Yıldırım1, Paweł Pławiak2, Ru-San Tan3
1Department of Computer Engineering, Munzur University, Tunceli, Turkey.
Insights
This study introduces a novel deep learning model for detecting 17 types of cardiac arrhythmias using long-duration electrocardiography (ECG) signals. The efficient 1D-CNN approach achieves high accuracy for real-time arrhythmia classification.
Area of Science:
- Biomedical Engineering
- Artificial Intelligence in Healthcare
- Cardiology
Background:
- Cardiovascular diseases pose a significant global health risk, necessitating improved diagnostic tools.
- Current automatic electrocardiography (ECG) signal analysis methods for arrhythmia detection are insufficient.
- Accurate and rapid detection of cardiac arrhythmias is crucial for effective disease prevention.
Purpose of the Study:
- To develop a novel deep learning approach for efficient and rapid classification of 17 cardiac arrhythmia types.
- To design an end-to-end deep learning model that integrates feature extraction and classification.
- To improve upon existing methods for automatic ECG analysis in terms of speed and accuracy.
Main Methods:
- Utilized a dataset of 1000 long-duration (10-second) ECG signal fragments from the MIT-BIH Arrhythmia database.
- Developed a novel 1D-Convolutional Neural Network (1D-CNN) model for end-to-end signal analysis.
- Focused on analyzing 10-second ECG fragments rather than individual QRS complexes for efficiency.
Main Results:
- Achieved an overall accuracy of 91.33% in classifying 17 distinct cardiac arrhythmia classes.
- Demonstrated a fast classification time of 0.015 seconds per sample, enabling real-time analysis.
- The 1D-CNN model efficiently combined feature extraction, selection, and classification in a single stage.
Conclusions:
- The proposed deep learning method offers an efficient, fast, and non-complex solution for cardiac arrhythmia detection.
- The 1D-CNN model represents a significant advancement over traditional methods requiring manual feature engineering.
- The developed approach is suitable for implementation in mobile health devices and cloud computing platforms for widespread accessibility.
Abstract:
This article presents a new deep learning approach for cardiac arrhythmia (17 classes) detection based on long-duration electrocardiography (ECG) signal analysis. Cardiovascular disease prevention is one of the most important tasks of any health care system as about 50 million people are at risk of heart disease in the world. Although automatic analysis of ECG signal is very popular, current methods are not satisfactory. The goal of our research was to design a new method based on deep learning to efficiently and quickly classify cardiac arrhythmias. Described research are based on 1000 ECG signal fragments from the MIT - BIH Arrhythmia database for one lead (MLII) from 45 persons. Approach based on the analysis of 10-s ECG signal fragments (not a single QRS complex) is applied (on average, 13 times less classifications/analysis). A complete end-to-end structure was designed instead of the hand-crafted feature extraction and selection used in traditional methods. Our main contribution is to design a new 1D-Convolutional Neural Network model (1D-CNN). The proposed method is 1) efficient, 2) fast (real-time classification) 3) non-complex and 4) simple to use (combined feature extraction and selection, and classification in one stage). Deep 1D-CNN achieved a recognition overall accuracy of 17 cardiac arrhythmia disorders (classes) at a level of 91.33% and classification time per single sample of 0.015 s. Compared to the current research, our results are one of the best results to date, and our solution can be implemented in mobile devices and cloud computing.
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