This study evaluates machine learning and deep learning models for classifying ECG signals from the PTB-XL dataset. It compares six models, including Random Forest, Logistic Regression, CNN, LSTM, and a complex CNN called ECG-Lense. The ECG-Lense model achieved the highest accuracy (80%) and ROC-AUC (90%) using raw signals and data augmentation. Findings support automated ECG analysis for improved diagnosis and future cardiac researchThis study evaluates machine learning and deep learning models for classifying ECG signals from the PTB-XL dataset. It compares six models, including Random Forest, Logistic Regression, CNN, LSTM, and a complex CNN called ECG-Lense. The ECG-Lense model achieved the highest accuracy (80%) and ROC-AUC (90%) using raw signals and data augmentation. Findings support automated ECG analysis for improved diagnosis and future cardiac research.
