Key result
Hybrid Residual Network detects arrhythmia with ~99% accuracy, outperforming conventional CNNs.
Why the study?
Current automated arrhythmia classification methods face challenges in detecting multiple cardiac abnormalities, especially with imbalanced ECG datasets.
Does a Hybrid Residual Network improve arrhythmia classification accuracy in ECG signals compared to conventional methods?
Does a Hybrid Residual Network improve arrhythmia classification accuracy in ECG signals compared to conventional methods?
Absolute Event Rate: 99.09% vs 98.24%
A novel Hybrid Residual Network approach achieves 99.09% accuracy in classifying arrhythmias from ECG signals, outperforming conventional methods.
May aid multi-arrhythmia detection on imbalanced ECGs; leaves open prospective clinical validation before practice change.
Arrhythmia detection in electrocardiogram (ECG) signals is essential for monitoring cardiovascular health. Current automated arrhythmia classification methods frequently encounter difficulties in detecting multiple cardiac abnormalities, particularly when dealing with imbalanced datasets. This paper proposes a novel deep learning approach for the detection and classification of arrhythmias in ECG signals using a Hybrid Residual Network (Hybrid ResNet). Our method employs a Hybrid Residual Network architecture that integrates standard convolution, depthwise separable convolution, and residual connections to enhance the feature extraction efficiency and classification accuracy. To guarantee superior input signals, we preprocess the ECG signals by removing baseline drift with a high-pass Butterworth filter, denoising via discrete wavelet transform, and segmenting heartbeat cycles through R-peak detection. Additionally, we rectify the class imbalance in the MIT-BIH Arrhythmia Database by applying the Synthetic Minority Oversampling Technique (SMOTE), therefore enhancing the model’s ability to detect infrequent arrhythmia types. The suggested system achieves a classification accuracy of 99.09% on the MIT-BIH dataset, surpassing conventional convolutional neural networks and other state-of-the-art methodologies. Compared to existing approaches, our strategy exhibits superior effectiveness and robustness in managing diverse irregular heartbeats and arrhythmias.
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Qi et al. (2024) studied Arrhythmia (n=47). Hybrid Residual Network (Hybrid ResNet) with SMOTE and DWT preprocessing vs. Traditional CNN and other state-of-the-art machine learning methods was evaluated on Classification accuracy. The proposed Hybrid Residual Network with SMOTE and discrete wavelet transform preprocessing achieved a classification accuracy of 99.09% on the MIT-BIH Arrhythmia Database, outperforming traditional CNNs.
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