Key result
Novel CNN detects cardiac arrhythmias with ~99% accuracy using wavelet transform scalograms.
Why the study?
Diagnosis of cardiac arrhythmia using ECG signals requires professional interpretation and is time-consuming, motivating development of automated detection methods.
Supports AI arrhythmia detection research; leaves open clinical validation before any practice consideration.
Electrocardiogram (ECG) signal is informative as well as non-invasive clinical tool to diagnose cardiac diseases of human heart. However, the diagnosis requires professionals’ clarification and is also time-consuming. To make the diagnosis proficient, a novel convolutional neural network (CNN) has been proposed for automatic arrhythmia detection. In this work, the ECG data collected from the MIT-BIH database have been preprocessed, and segmented in short ECG segments of 60 s. Then, all these segments have been transformed into scalogram images obtained from time-frequency analysis using continuous wavelet transform (CWT). Finally, these scalogram images have been exploited as an input for our designed CNN classifier to classify cardiac arrhythmia. In this approach, the overall accuracy, sensitivity, and specificity are 99.39%, 98.79%, and 100% respectively. Proposed CNN model has significant advantages, and it can be used to differentiate the healthy and arrhythmic patients effectively.
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Mohonta et al. (2022) studied Cardiac arrhythmia. Convolutional neural network (CNN) based on continuous wavelet transform (CWT) scalogram images was evaluated on Automatic arrhythmia detection (accuracy, sensitivity, specificity). A novel convolutional neural network using continuous wavelet transform scalogram images classified cardiac arrhythmia with an overall accuracy of 99.39%, sensitivity of 98.79%, and specificity of 100%.
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