Article Detail

Sercan TOHMA

Keywords

ECG Arrhythmia Classification MIT-BIH PTB-DB Machine Learning Histogram-Based Gradient Boosting.

Doi : 10.5281/zenodo.18664503

Abstract

Abstract In this study, two publicly available electrocardiogram (ECG) datasets were used to address (i) five-class heartbeat classification on MIT-BIH and (ii) binary (normal/abnormal) cardiac anomaly detection on PTB-DB. Raw time series samples (187-point segments) were used directly as feature vectors; after z-score standardization, Random Forest, Histogram-Based Gradient Boosting, Decision Tree, Naive Bayes, and Logistic Regression models were trained. Experimental evaluation was performed using accuracy (Acc), precision (Prec), recall (Rec), F1, and AUC metrics. The results indicated that the best performance in both datasets was achieved with the Histogram-Based Gradient Boosting model (MIT-BIH: Acc = 0.9801, AUC = 0.9930; PTB-DB: Acc = 0.9825, AUC = 0.9945). The findings show that classical machine learning models, with appropriate preprocessing and parameterization, can offer competitive performance in ECG-based arrhythmia and cardiac anomaly classification problems.

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