ArticleIEEE journal of translational engineering in health and medicine2023
Arrhythmia Disease Diagnosis Based on ECG Time-Frequency Domain Fusion and Convolutional Neural Network.
Article in IEEE journal of translational engineering in health and medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Diagnostic performance of the ECG-SMART AI score for detecting angiographically significant coronary artery disease in patients undergoing elective coronary angiography.PLOS digital health · 2026Article
- Development and explanation of electrocardiogram-based deep learning for predicting short-term mortality in heart failure patients.Journal of global health · 2026Article
- DyGAT-FTNet: A Dynamic Graph Attention Network for Multi-Sensor Fault Diagnosis and Time-Frequency Data Fusion.Sensors (Basel, Switzerland) · 2025Article
- A novel method of BiFormer with temporal-spatial characteristics for ECG-based PVC detection.Frontiers in physiology · 2025Article
- ECG-Mamba: Cardiac Abnormality Classification With Non-Uniform-Mix Augmentation on 12-Lead ECGs.IEEE journal of translational engineering in health and medicine · 2025Article
- An improved electrocardiogram arrhythmia classification performance with feature optimization.BMC medical informatics and decision making · 2024Article
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Authors and funding
9 authors.
Funding
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Abstract
Electrocardiogram (ECG) signals are often used to diagnose cardiac status. However, most of the existing ECG diagnostic methods only use the time-domain information, resulting in some obviously lesion information in frequency-domain of ECG signals are not being fully utilized. Therefore, we propose a method to fuse the time and frequency domain information in ECG signals by convolutional neural network (CNN). First, we adapt multi-scale wavelet decomposition to filter the ECG signal; Then, R-wave localization is used to segment each individual heartbeat cycle; And then, the frequency domain information of this heartbeat cycle is extracted via fast Fourier transform. Finally, the temporal information is spliced with the frequency domain information and input to the neural network for classification. The experimental results show that the proposed method has the highest recognition accuracy (99.43%) of ECG singles compared with state-of-the-art methods. Clinical and Translational Impact Statement- The proposed ECG classification method provides an effective solution for ECG interrogation to quickly diagnose the presence of arrhythmia in a patient from the ECG signal. It can increase the efficiency of the interrogating physician by aiding diagnosis.
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