LightGBM-Based Classification of Heart Failure Phenotypes Using Morpho-Energy Features from High-Resolution ECG.
MA, G., S, K., R, D., & CV, S. (2026). LightGBM-Based Classification of Heart Failure Phenotypes Using Morpho-Energy Features from High-Resolution ECG.. Sensors (Basel, Switzerland). https://doi.org/10.3390/s26113397
MA G, S K, R D, CV S. LightGBM-Based Classification of Heart Failure Phenotypes Using Morpho-Energy Features from High-Resolution ECG.. Sensors (Basel, Switzerland). 2026; doi: 10.3390/s26113397
MA G, S K, R D, et al. LightGBM-Based Classification of Heart Failure Phenotypes Using Morpho-Energy Features from High-Resolution ECG.[J]. Sensors (Basel, Switzerland). 2026. DOI: 10.3390/s26113397.
@article{ma2026,
author = {Gader MA and Karmani S and Djemal R and Sakuyama CV},
title = {LightGBM-Based Classification of Heart Failure Phenotypes Using Morpho-Energy Features from High-Resolution ECG.},
journal = {Sensors (Basel, Switzerland)},
year = {2026},
doi = {10.3390/s26113397},
note = {PMID: 42280915},
}
TY - JOUR AU - Gader MA AU - Karmani S AU - Djemal R AU - Sakuyama CV TI - LightGBM-Based Classification of Heart Failure Phenotypes Using Morpho-Energy Features from High-Resolution ECG. T2 - Sensors (Basel, Switzerland) PY - 2026 DO - 10.3390/s26113397 AN - PMID:42280915 ER -
Heart failure (HF) remains a major global health challenge, necessitating accurate yet accessible diagnostic tools. While the left ventricular ejection fraction (LVEF) is the primary metric for classifying HF into preserved (HFpEF), mid-range (HFmrEF), and reduced (HFrEF) phenotypes, conventional imaging modalities such as echocardiography are resource intensive. In contrast, the electrocardiogram (ECG) offers a low-cost, non-invasive alternative for continuous cardiac assessment. This paper proposes a multi-algorithm artificial intelligence (AI) framework for automated HF phenotype classification using high-resolution ECG signals from 303 patients with chronic heart failure from the MUSIC cohort. After preprocessing (normalization, bandpass filtering), we employed a hybrid approach combining the Pan-Tompkins algorithm for robust R-peak detection with the NeuroKit2 toolbox for the precise delineation of P, Q, S, and T waves. ECG recordings were then segmented using an adaptive beat-centric windowing strategy. From the segmented beats, we extracted a comprehensive set of temporal, morphological, and energy-based features, including RR, QRS, and QT intervals, along with P-wave, QRS-complex, and T-wave energies. These features were used to train and evaluate several ensemble machine learning models-Random Forest, XGBoost, CatBoost, LightGBM, and a stacking classifier-using a stratified 70-15-15 train-validation-test split with 5-fold cross-validation. The LightGBM model achieved the highest performance with a test accuracy of 98.45%, an AUC of 0.9989, and a macro F1-score of 0.9804, outperforming other ensembles and the stacking classifier. The results demonstrate that an AI-driven analysis of ECG-derived morpho-energy features can serve as a reliable, non-invasive screening tool for the accurate and early discrimination of HF phenotypes, potentially supporting clinical decision making and improving patient management in resource-limited settings.