Improved accuracy of PCG signal classification for myocardial infarction biomarker using automatic feature selection and boosting process

Abstract

Myocardial infarction (MI) is a leading global health concern, typically diagnosed using ECG, biomarkers, or imaging, which costly or unavailable in low-resource settings. This study presents a non-invasive, machine learning-based approach using phonocardiogram (PCG) signals for classifying normal, ST-elevation MI (STEMI), and non-ST-elevation MI (NSTEMI). The proposed approach leverages the acoustic signatures of cardiac mechanical activity, capturing subtle variations in heart sound morphology and timing associated with ischemic myocardial dysfunction. The processing pipeline included PCG acquisition via electronic stethoscope, band-pass filtering, envelope-based segmentation, feature extraction, and selection using Mutual Information and K-best ranking. We evaluated the method using a diverse dataset of 104 subjects from Indonesia and Japan to ensure generalizability across ethnic and physiological variations. Eighteen key features with mutual information values up to 0.82 bits were used to train AdaBoost and Gradient Boosting models. Without parameter tuning, these models achieved 88.30% and 93.00% accuracy, respectively. After optimization, AdaBoost reached 94.00% accuracy, and Gradient Boosting achieved 98.30% accuracy and a 95.10% F1-score. These results outperform previous bagging-based methods of 86.00% accuracy, demonstrating improved accuracy and robustness. This work highlights the potential of PCG-based MI detection as a low-cost, non-invasive diagnostic alternative, particularly valuable for early screening and triage in resource-limited healthcare settings.