Machine learning analysis of ECG signals distinguished all COVID-19 severity stages with 78.57% accuracy, and achieved 100% accuracy for differentiating low versus moderate severity.
Observational
Does machine learning-based non-linear analysis of ECG signals accurately discriminate COVID-19 patients with different degrees of severity?
Machine learning analysis of non-linear ECG features can effectively distinguish between different severity stages of COVID-19, offering a potential tool for rapid assessment.
This study highlights the potential of an Electrocardiogram (ECG) as a powerful tool for early diagnosis of COVID-19 in critically ill patients with limited access to CT–Scan rooms. In this investigation, 3 categories of patient status were considered: Low, Moderate, and Severe. For each patient, 2 different body positions have been used to collect 2 ECG signals. Then, from each collected signal, 10 non-linear features (Energy, Approximate Entropy, Logarithmic Entropy, Shannon Entropy, Hurst Exponent, Lyapunov Exponent, Higuchi Fractal Dimension, Katz Fractal Dimension, Correlation Dimension and Detrended Fluctuation Analysis) were extracted every 1s ECG time-series length to serve as entries for 19 Machine learning classifiers within a leave-one-out cross-validation procedure. Four different classification scenarios were tested: Low vs. Moderate, Low vs. Severe, Moderate vs. Severe and one Multi-class comparison (All vs. All). The classification report results were: (1) Low vs. Moderate - 100% of Accuracy and 100% of F1–Score; (2) Low vs. Severe - Accuracy of 91.67% and an F1–Score of 94.92%; (3) Moderate vs. Severe - Accuracy of 94.12% and an F1–Score of 96.43%; and (4) All vs All - 78.57% of Accuracy and 84.75% of F1–Score. The results indicate that the applied methodology could be considered a good tool for distinguishing COVID-19’s different severity stages using ECG signals. The findings highlight the potential of ECG as a fast and effective tool for COVID-19 examination. In comparison to previous studies using the same database, this study shows a 7.57% improvement in diagnostic accuracy for the All vs All comparison.
Ribeiro et al. (Sat,) conducted a observational in COVID-19. Machine learning-based non-linear analysis of ECG signals was evaluated on Diagnostic accuracy and F1-Score for classifying COVID-19 severity stages. Machine learning analysis of ECG signals distinguished all COVID-19 severity stages with 78.57% accuracy, and achieved 100% accuracy for differentiating low versus moderate severity.