Key result
Convolutional neural networks trained on EKG scalograms demonstrated an accuracy greater than 90% across all models for diagnosing various heart disorders and arrhythmias.
Why the study?
To evaluate whether deep learning algorithms could accurately diagnose heart disorders from EKG signals using wavelet transformations to generate images and provide a complement to traditional EKG views.
Can deep learning algorithms using wavelet transformations accurately diagnose heart disorders from EKG signals?
Can deep learning algorithms using wavelet transformations accurately diagnose heart disorders from EKG signals?
Answers are grounded in this paper and cite their sources.
Deep learning using CNNs and wavelet transformations of EKGs into scalograms achieves >90% accuracy in diagnosing heart disorders, offering a potential complementary tool to traditional EKG interpretation.
Purpose: The purpose of this study was to evaluate the potential of deep learning as a tool for computer-aided diagnosis of heart disorders based on EKG signals, using wavelet transformations to generate images. The research question was whether deep learning algorithms could accurately diagnose heart disorders and provide a valuable complement to traditional EKG views. Methods: We trained five Convolutional Neural Networks (CNNs) using EKG data obtained from the Physionet public database. The algorithms were developed using MATLAB version 2018b and the toolboxes for digital signal processing, neural networks, and wavelets. We evaluated the performance of the CNNs using accuracy, sensitivity, specificity, positive predictive value, and negative predictive value as metrics. Results: The CNNs demonstrated accuracy greater than 90%, and achieved good performance for the other evaluated parameters. We also identified that the representation of EKGs as scalograms showed potential for use as a complement to traditional EKG views. Conclusion: Our findings demonstrate that deep learning is a promising tool for diagnosing heart disorders based on EKG signals, and can be a valuable complement to traditional EKG views. While automated diagnoses should not replace clinical judgment, deep learning can provide additional support to healthcare professionals. Further research should explore the potential of deep learning for medical diagnosis and the use of scalograms as a complementary tool in clinical practice.
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Albernaz et al. (2023) studied Heart disorders and arrhythmias (n=253). Convolutional Neural Networks (CNNs) using EKG scalograms was evaluated on Accuracy in distinguishing altered from non-altered EKGs (CNN 1). Convolutional neural networks trained on EKG scalograms demonstrated an accuracy greater than 90% across all models for diagnosing various heart disorders and arrhythmias.
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