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Advanced Time-Frequency Methods for ECG Waves Recognition
Ala'a Zyout1, Hiam Alquran1, Wan Azani Mustafa2,3
1Department of Biomedical Systems and Informatics Engineering, Yarmouk University, Irbid 21163, Jordan.
Insights
This study shows that analyzing a single electrocardiogram (ECG) wave using time-frequency analysis and deep learning can accurately detect heart rhythms like normal, tachycardia, and bradycardia.
Area of Science:
- Cardiology
- Biomedical Engineering
- Artificial Intelligence
Background:
- Electrocardiogram (ECG) wave recognition is crucial for diagnosing heart conditions.
- Traditional methods struggle to differentiate normal, tachycardia, and bradycardia rhythms using only time or frequency domains.
Purpose of the Study:
- To evaluate the effectiveness of iris-spectrogram and scalogram time-frequency representations for ECG beat wave analysis.
- To assess the performance of ResNet101 and ShuffleNet deep convolutional neural networks (CNNs) for rhythm classification.
Main Methods:
- Utilized iris-spectrogram and scalogram for spectral representation of individual ECG beat waves (P, QRS, T).
- Employed ResNet101 and ShuffleNet CNN architectures for feature extraction and classification.
- Compared classification accuracy across different wave segments and spectrum representations.
Main Results:
- Achieved a 98.3% accuracy using ResNet101 with T-wave scalogram for rhythm detection.
- Obtained 94.4% accuracy with ResNet101 and QRS-wave iris-spectrogram.
- Demonstrated the efficacy of time-frequency analysis on single ECG waves for rhythm classification.
Conclusions:
- Deep features derived from time-frequency representations of single ECG waves enable accurate detection of basic heart rhythms.
- Scalogram of the T-wave combined with ResNet101 shows superior performance for distinguishing normal, tachycardia, and bradycardia rhythms.
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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