Key result
A wavelet-based technique using Continuous Wavelet Transform and a pre-trained AlexNet Deep CNN achieved an accuracy of 97.14% in classifying ECG signals.
Why the study?
Classification and prediction of heart diseases based on ECG signals have become increasingly important, prompting the development of techniques to calculate heart rate and classify ECG signals accurately.
Does a wavelet-based technique using AlexNet CNN accurately classify ECG signals for arrhythmia?
Does a wavelet-based technique using AlexNet CNN accurately classify ECG signals for arrhythmia?
A deep learning approach using AlexNet and wavelet transforms can classify ECG signals for arrhythmia with high accuracy (97.14%).
Classification and prediction of heart diseases based on ECG signals have become increasingly important for patients and doctors over the last decade. The fundamental objective of this work is to calculation of heart-rate and create a comprehensive learning-based technique that can classify ECG signals more accurately. We collected all data from PhysioNet for doing this work. We calculated heart rate using the Discrete Wavelet Transform (DWT) and trained a pre-trained Convolutional Neural Network (CNN), specifically AlexNet, to classify ECG signals. To begin, we extracted a spectrogram for each of the signals and transformed them to RGB images using the Continuous Wavelet Transform (CWT); these images were then put into AlexNet and trained with minor specification adjustments. The study's findings indicate that our technique achieves an accuracy of 97.14 %
No takes yet. Share an insight, caveat, or question.
Sajjad et al. (2022) studied Arrhythmia. Wavelet-Based Heart-Rate Detection and ECG Classification using AlexNet Deep CNN was evaluated on Classification accuracy. A wavelet-based technique using Continuous Wavelet Transform and a pre-trained AlexNet Deep CNN achieved an accuracy of 97.14% in classifying ECG signals.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: