Key result
Novel ANN-based algorithm detects ~100% of true beats across normal and arrhythmic signals.
Why the study?
Automated ECG signal analysis methods for cardiac arrhythmia classification require improved performance through novel feature extraction and modeling approaches.
A novel ANN-based framework for ECG feature extraction and classification improves automated detection of cardiac arrhythmias.
Should not yet change arrhythmia detection practice; leaves open prospective validation of multi-feature ANN classifiers.
Automated bioelectric signal analysis has an important application in the wisdom medical care. In this work, we focus on ECG-signal and address a novel approach for cardiac arrhythmia diseases classification. We designed a novel analysis framework which extract different feature transformations from ECG signals. And we trained the ANN model for multi-feature to obtain the prediction. Finally, we tested our approach on the public database of MIT-BIH arrhythmia. And the results of experiments on the database demonstrate our model has better classification performance than other approaches.
No takes yet. Share an insight, caveat, or question.
Amhia et al. (2022) studied Cardiac arrhythmia (n=19). Hilbert transform with adaptive thresholding and Artificial Neural Network (ANN) vs. Traditional signal denoising using Discrete Wavelet Transform (DWT) was evaluated on Beat detection accuracy and classification performance. The proposed Hilbert transform and ANN-based algorithm detected 44,325 beats from 44,329 true beats, achieving 96.5% accuracy for normal signals, 95.8% for bradycardia, and 98.1% for tachycardia.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: