Key result
A modified 1-D U-Net architecture for identifying the onset and offset of P, QRS, and T-waves in ECG signals demonstrated superior performance compared to state-of-the-art techniques.
Why the study?
Existing semi- and fully automatic ECG beat classification systems are limited by extensive data requirements and low performance.
A novel 1-D U-Net architecture provides superior performance for automated ECG beat classification and fiducial point identification compared to existing methods.
Electrocardiogram (ECG) signals carried important clinical information in the form of intervals and amplitude or morphology. Therefore, it is very important to identify these fiducial points effectively. The major limitations of the existing semi- and fully automatic ECG beat classification systems are extensive data requirements and low performance. This letter proposes a modified 1-D U-Net architecture to find the exact locations of the important waves in the ECG signal. The proposed network utilized 1-D convolutions instead of 2-D, which is different from the traditional U-Net. The encoder and decoder are the main components of the proposed 1-D U-Net, where the encoder is designed to extract high-level features of the input data. The encoder contains two downsampling stages, and the output of each stage in the encoder is sent to the corresponding decoder layer for further convolutional operations. The output of the decoder section concatenates with the respective encoder, which enables the network to perceive low-level and high-level features. The onset and offset of P, QRS, and T-waves in the ECG signal are identified using the proposed technique. The proposed method is validated on two publicly available databases, i.e., LUDB and MIT-BIH. The experimental results demonstrate that the performance of the proposed method is superior to the state-of-the-art techniques.
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Prakash et al. (2023) studied ECG beat classification. Modified 1-D U-Net architecture vs. State-of-the-art techniques was evaluated on Identification of onset and offset of P, QRS, and T-waves. A modified 1-D U-Net architecture for identifying the onset and offset of P, QRS, and T-waves in ECG signals demonstrated superior performance compared to state-of-the-art techniques.
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