Key result
Spectral ECG machine learning distinguishes moderate from severe heart failure in Chagas disease with ~0.83 AUC.
Why the study?
Automated, low-cost risk stratification tools are needed to improve triage and clinical decision-making for heart failure severity in Chagas disease, especially in resource-limited settings.
Does machine learning-based ECG power spectral analysis accurately classify heart failure severity in patients with Chagas disease?
Cross-Sectional (n=380)
Yes
Does machine learning-based ECG power spectral analysis accurately classify heart failure severity in patients with Chagas disease?
Machine learning analysis of ECG power spectral density can automatically stratify heart failure severity in Chagas disease, offering a potential low-cost triage tool.
Answers are grounded in this paper and cite their sources.
May facilitate non-invasive HF severity classification in Chagas disease; extends spectral ML methods to short ECGs but leaves open prospective validation.
This study presents a machine learning methodology to automatically classify heart failure severity in Chagas disease (CD) patients using non-invasive 24-hour ECG-Holter signals. Following American Heart Association (AHA) guidelines, the cohort was stratified into three Left Ventricular Ejection Fraction (LVEF)-based severity groups: Normal (LVEF ≥ 0.50, n=197), Moderate (0.40 ≤ LVEF < 0.50, n=106), and Severe (LVEF < 0.40, n=77), totaling N=380 patients. From short 10-second ECG segments, we extracted eleven spectral features derived from the power spectral density (PSD). Class imbalance was addressed through oversampling applied to the training folds. All classifiers were evaluated over 50 random stratified train-test splits (80/20) across three pairwise tasks (Normal vs. Moderate, Normal vs. Severe, Moderate vs. Severe). Analysis revealed a consistent leftward shift in PSD, with increased low-frequency power in more severe cases, consistent with morphological ECG changes including P-wave attenuation, QRS alterations, and ST-segment shifts. Using this spectral biomarker, the best models achieved mean AUC/PR-AUC values of 0.79/0.76 for Normal vs. Severe and 0.83/0.85 for Moderate vs. Severe across 50 random states. The Normal vs. Moderate task showed moderate separability (AUC = 0.75, PR-AUC = 0.72). These findings highlight the potential of power spectral ECG analysis as a low-cost, fully automated tool for risk stratification in CD. The methodology shows promise for improving triage and clinical decision-making in resource-limited settings where CD remains highly prevalent.
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Ribeiro et al. (2026) conducted a cross-sectional in Chagas disease heart failure (n=380). Machine learning classification using ECG power spectral biomarkers was evaluated on AUC for Moderate vs. Severe classification. A machine learning model using power spectral ECG biomarkers classified heart failure severity in Chagas disease patients, achieving an AUC of 0.8348 for distinguishing moderate from severe cases.
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