ArticleDiagnostics (Basel, Switzerland)2023
Advanced Time-Frequency Methods for ECG Waves Recognition.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- AI-Based QRS Onset Detection in the Early Ventricular Activation Site ECGs.IEEE journal of biomedical and health informatics · 2026Article
- Deep learning-based prediction of atrial fibrillation from polar transformed time-frequency electrocardiogram.PloS one · 2025Article
- Wearable sensors-based assistive technologies for patient health monitoring.Frontiers in bioengineering and biotechnology · 2025Article
- Edge-AI Enabled Wearable Device for Non-Invasive Type 1 Diabetes Detection Using ECG Signals.Bioengineering (Basel, Switzerland) · 2024Article
- Delineation of 12-Lead ECG Representative Beats Using Convolutional Encoder-Decoders with Residual and Recurrent Connections.Sensors (Basel, Switzerland) · 2024Article
- [An image classification method for arrhythmias based on Gramian angular summation field and improved Inception-ResNet-v2].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- Editorial on Special Issue "Medical Data Processing and Analysis".Diagnostics (Basel, Switzerland) · 2023Article
- A novel method for objective quantification of apathy based on gaze and physiological reactivity to stimuli presented in a virtual reality environment.Alzheimer's & dementia (Amsterdam, Netherlands)Article
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Authors and funding
4 authors.
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Abstract
ECG wave recognition is one of the new topics where only one of the ECG beat waves (P-QRS-T) was used to detect heart diseases. Normal, tachycardia, and bradycardia heart rhythm are hard to detect using either time-domain or frequency-domain features solely, and a time-frequency analysis is required to extract representative features. This paper studies the performance of two different spectrum representations, iris-spectrogram and scalogram, for different ECG beat waves in terms of recognition of normal, tachycardia, and bradycardia classes. These two different spectra are then sent to two different deep convolutional neural networks (CNN), i.e., Resnet101 and ShuffleNet, for deep feature extraction and classification. The results show that the best accuracy for detection of beats rhythm was using ResNet101 and scalogram of T-wave with an accuracy of 98.3%, while accuracy was 94.4% for detection using iris-spectrogram using also ResNet101 and QRS-Wave. Finally, based on these results we note that using deep features from time-frequency representation using one wave of ECG beat we can accurately detect basic rhythms such as normal, tachycardia, and bradycardia.
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