Abstract
Arrhythmia is a kind of cardiac conduction disorder those result in irregular heartbeats. The electrocardiograph (ECG) signal may identify conduction system abnormalities. However, its visual analysis is challenging and time-consuming. An automated system for cardiac disorder detection may help in early and prompt diagnosis of diseases. In this paper, stationary wavelet transform (SWT) was used for pre-processing of the raw ECG signal before the segmentation and normalization process. Thereafter, recurrent neural network (RNN), gated recurrent units (GRU), bi-directional long short-term memory (Bi-LSTM) have been implemented for classification of normal, left bundle branch block (L-BBB), right bundle branch block l(R-BBB), premature atrial contraction (PAC), and premature ventricular contraction (PVC) beats. Bi-LSTM networks have shown best accuracy of 99.72% among all three implemented models. This demonstrates that this model is appropriate for computer-aided diagnosis of heartbeats.
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MIT-BIH arrhythmia data that support the findings of this study are available in https://physionet.org.
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Sharma, L.D., Rahul, J., Aggarwal, A. et al. An improved cardiac arrhythmia classification using stationary wavelet transform decomposed short duration QRS segment and Bi-LSTM network. Multidim Syst Sign Process 34, 503–520 (2023). https://doi.org/10.1007/s11045-023-00875-x
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DOI: https://doi.org/10.1007/s11045-023-00875-x