ArticleFrontiers in cardiovascular medicine2022
Electrocardiogram classification using TSST-based spectrogram and ConViT.
Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.
- A Systematic Survey of Data Augmentation of ECG Signals for AI Applications.Sensors (Basel, Switzerland) · 2023Pooled it
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- Intelligent cardiovascular disease diagnosis using deep learning enhanced neural network with ant colony optimization.Scientific reports · 2024Article
- Predicting angiographic coronary artery disease using machine learning and high-frequency QRS.BMC medical informatics and decision making · 2024Article
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- Quantum-inspired seagull optimised deep belief network approach for cardiovascular disease prediction.PeerJ. Computer science · 2024Article
- Advancements in tissue engineering for cardiovascular health: a biomedical engineering perspective.Frontiers in bioengineering and biotechnology · 2024Review
- A novel approach for denoising electrocardiogram signals to detect cardiovascular diseases using an efficient hybrid scheme.Frontiers in cardiovascular medicine · 2024Article
- Influence of parent vessel feature on the risk of internal carotid artery aneurysm rupture via computational method.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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