Enhancing ECG Analysis with a Hybrid Deep Learning Approach: Automatic Detection of Significant Features

Cardiovascular diseases (CVDs) are the main cause of death worldwide, with arrhythmias posing significant risks. The interpretation of ECG signals requires professional expertise and time, which has limited its accessibility to the general public. This research introduces a novel hybrid deep learning technique for the automatic detection of key features (QRS complex, P and T waves) in electrocardiogram (ECG) signals. The method integrates two Convolutional Neural Networks (CNNs) and two Bidirectional Long Short-Term Memory Networks (BiLSTMs) to form a 2D-CNN-BiLSTM model. The model is trained and evaluated using the "QT Database", a publicly available dataset. ECG signals are first downsampled and then pre-processed using a local linear regression technique to remove the baseline, and the Continuous Wavelet Transform (CWT) to convert 1D ECG signals into 2D images. The model demonstrates superior classification performance in detecting P waves, QRS complexes, and T waves, compared to existing methods. The suggested approach shows promise for improving the precision and effectiveness of ECG analysis and advancing automated cardiac diagnostic techniques.