Key result
A multimodal feature fusion model utilizing a dual channel deep neural network achieved an average F1 score of 0.85 and average accuracy of 0.97 for classifying nine types of arrhythmias.
Why the study?
Effectively fusing information across 12 leads is difficult, and current automatic arrhythmia diagnosis methods rely on single-mode features from 1D sequences while ignoring frequency domain features, making accurate detection challenging.
A novel multimodal feature fusion deep learning model using 12-lead ECGs demonstrated high accuracy (0.97) and F1 score (0.85) for automated arrhythmia classification.
Supports AI arrhythmia detection development; leaves open prospective clinical validation before practice adoption.
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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Yang et al. (2023) studied Arrhythmias. Multimodal feature fusion model based on a dual channel deep neural network and attention mechanism was evaluated on Classification of nine types of arrhythmia signals (average F1 score and average accuracy). A multimodal feature fusion model utilizing a dual channel deep neural network achieved an average F1 score of 0.85 and average accuracy of 0.97 for classifying nine types of arrhythmias.
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