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Automatic Classification Method of Arrhythmias Based on 12-Lead Electrocardiogram
Insights
This study introduces a novel deep learning model for accurate arrhythmia detection using 12-lead electrocardiograms. The multimodal approach effectively fuses time and frequency domain features, improving diagnostic accuracy for cardiovascular diseases.
Area of Science:
- Cardiology
- Biomedical Engineering
- Artificial Intelligence in Medicine
Background:
- Cardiovascular diseases, particularly arrhythmias, are a leading global cause of mortality.
- Electrocardiograms (ECGs) are crucial for diagnosing arrhythmias, but effectively utilizing 12-lead data remains challenging.
- Existing automated methods often overlook frequency domain features and struggle with information fusion across leads.
Purpose of the Study:
- To develop a highly accurate and generalizable automated arrhythmia detection algorithm using 12-lead ECGs.
- To address limitations in current methods by integrating multimodal feature extraction and fusion.
- To improve the classification of various arrhythmia types.
Main Methods:
- A dual-channel deep neural network was employed to extract features from both 1D ECG sequences and 2D time-frequency representations.
- An attention mechanism was integrated to effectively fuse critical information from all 12 leads.
- The model was trained and validated on a mixed dataset encompassing nine arrhythmia types.
Main Results:
- The developed model achieved an average F1 score of 0.85 and an average accuracy of 0.97.
- The multimodal feature fusion approach successfully integrated diverse ECG signal characteristics.
- Experimental results demonstrated stable and reliable performance across different arrhythmia classifications.
Conclusions:
- The proposed model offers a promising advancement in automated arrhythmia detection.
- Effective fusion of multimodal ECG features, including time and frequency domains, enhances diagnostic accuracy.
- The algorithm shows significant potential for practical clinical application in cardiovascular disease management.
Abstract:
Cardiovascular disease is one of the main causes of death worldwide. Arrhythmias are an important group of cardiovascular diseases. The standard 12-lead electrocardiogram signals are an important tool for diagnosing arrhythmias. Although 12-lead electrocardiogram signals provide more comprehensive arrhythmia information than single-lead electrocardiogram signals, it is difficult to effectively fuse information between different leads. In addition, most of the current researches working on automatic diagnosis of cardiac arrhythmias are based on modeling and analysis of single-mode features extracted from one-dimensional electrocardiogram sequences, ignoring the frequency domain features of electrocardiogram signals. Therefore, developing an automatic arrhythmia detection algorithm based on 12-lead electrocardiogram with high accuracy and strong generalization ability is still challenging. In this paper, a multimodal feature fusion model based on the mechanism is developed. This model utilizes a dual channel deep neural network to extract different dimensional features from one-dimensional and two-dimensional electrocardiogram time-frequency maps, and combines attention mechanism to effectively fuse the important features of 12-lead, thereby obtaining richer arrhythmia information and ultimately achieving accurate classification of nine types of arrhythmia signals. This study used electrocardiogram signals from a mixed dataset to train, validate, and evaluate the model, with an average of F1 score and average accuracy reached 0.85 and 0.97, respectively. Experimental results show that our algorithm has stable and reliable performance, so it is expected to have good practical application potential.
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Definition
An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
Parts of an ECG
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Types of Arrhythmias
Sinus Node Arrhythmias
Sinus Bradycardia: Originating from the sinoatrial (SA) node, sinus bradycardia involves slower impulses, resulting in a heart rate of less than 60 beats per minute (bpm). Causes include sleep, vagal stimulation, beta-blockers, hypothyroidism,...