Abstract
Electrocardiograms (ECGs) are essential tools for diagnosing cardiac conditions. However, analyzing 12-lead ECG signals manually is time-consuming, making automated classification crucial for efficient and accurate diagnosis. This study investigates both handcrafted and deep learning (DL)-based feature extraction techniques for classifying 12-lead ECG signals. The aim is to enhance diagnostic accuracy and efficiency. We extracted QRSTP peaks and computed various time-domain features, including heart rate, heart rate variability ( H R V ), R R intervals, median R-R intervals, SDNN, RMSSD, and PNN60, based on the R -peaks during the handcrafted feature extraction process from the 12 -lead ECG signals. Additionally, we applied several DL algorithms, including CNN, ResNet18, VGG16, and DenseNet83, to extract new global features from the raw ECG signals. We used a merged dataset from five different sources, including the "CPC Database" and "CPC Database Extra" from the China Physiological Signal Challenge 2018 (CPSC2018), the "St Petersburg INCART 12-lead Arrhythmia Database," the "PTB Diagnostic ECG Database" and "PTBXL" from the Physikalisch-Technische Bundesanstalt (PTB), the "Georgia Database," and an undisclosed American database. The dataset comprises 22,797 12-lead ECG recordings. We have conducted a series of experiments for performance evaluation for feature extraction and training models. The experimental results show that combining handcrafted and DL features outperforms the DL-based methods in improving classification performance. Both quantitative and qualitative studies, along with ablation experiments, are conducted to further validate our approach.