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Updated: Aug 23, 2025
Computer-based Multitaper Spectrogram Program for Electroencephalographic Data
Published on: November 13, 2019
Electrocardiogram classification using TSST-based spectrogram and ConViT
Pingping Bing1, Yang Liu2, Wei Liu2
1Academician Workstation, Changsha Medical University, Changsha, China.
Insights
This study introduces ConViT, a deep learning model for Electrocardiogram (ECG) arrhythmia classification, achieving 99.5% accuracy. The novel approach enhances cardiovascular disease diagnosis by improving detection of irregular heart rhythms.
Area of Science:
- Cardiology
- Artificial Intelligence
- Signal Processing
Background:
- Electrocardiogram (ECG) is crucial for diagnosing arrhythmias and related cardiovascular diseases.
- ECG arrhythmia classification presents significant challenges.
- Existing methods often struggle with data imbalance and feature extraction.
Purpose of the Study:
- To develop a novel deep learning model for accurate ECG arrhythmia classification.
- To integrate the strengths of Convolutional Neural Networks (CNN) and Vision Transformers (ViT).
- To address challenges like class imbalance and enhance feature extraction for improved diagnostic accuracy.
Main Methods:
- A novel deep learning model, Convolutional Vision Transformer (ConViT), combining CNN and ViT architectures.
- Gated Positional Self-Attention (GPSA) layers to integrate convolutional inductive bias with attention mechanisms.
- Time-Reassigned Synchrosqueezing Transform (TSST) for enhanced time-frequency feature extraction.
- SMOTE algorithm for data augmentation and Focal Loss (FL) for minority-class weighting to handle data imbalance.
Main Results:
- The proposed ConViT model achieved an overall accuracy of 99.5% on the MIT-BIH arrhythmia database.
- High performance metrics, including specificity, F1-Score, and Matthews Correlation Coefficient (MCC) exceeding 94% for supraventricular ectopic beats (S) and ventricular ectopic beats (V).
- Demonstrated superiority over existing methods in ECG arrhythmia classification.
Conclusions:
- The ConViT model offers a powerful and accurate solution for ECG arrhythmia classification.
- The integration of advanced deep learning techniques and signal processing methods significantly improves diagnostic capabilities.
- This approach holds promise for enhancing the early detection and management of cardiovascular diseases.
Abstract:
As an important auxiliary tool of arrhythmia diagnosis, Electrocardiogram (ECG) is frequently utilized to detect a variety of cardiovascular diseases caused by arrhythmia, such as cardiac mechanical infarction. In the past few years, the classification of ECG has always been a challenging problem. This paper presents a novel deep learning model called convolutional vision transformer (ConViT), which combines vision transformer (ViT) with convolutional neural network (CNN), for ECG arrhythmia classification, in which the unique soft convolutional inductive bias of gated positional self-attention (GPSA) layers integrates the superiorities of attention mechanism and convolutional architecture. Moreover, the time-reassigned synchrosqueezing transform (TSST), a newly developed time-frequency analysis (TFA) method where the time-frequency coefficients are reassigned in the time direction, is employed to sharpen pulse traits for feature extraction. Aiming at the class imbalance phenomena in the traditional ECG database, the smote algorithm and focal loss (FL) are used for data augmentation and minority-class weighting, respectively. The experiment using MIT-BIH arrhythmia database indicates that the overall accuracy of the proposed model is as high as 99.5%. Furthermore, the specificity (Spe), F1-Score and positive Matthews Correlation Coefficient (MCC) of supra ventricular ectopic beat (S) and ventricular ectopic beat (V) are all more than 94%. These results demonstrate that the proposed method is superior to most of the existing methods.
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