Enhancing ECG Analysis through Deep Learning-Based Waveform Segmentation: A Time-Frequency Approach

This research work focuses on the automation of electrocardiogram (ECG) waveform annotation and segmentation utilizing deep learning techniques. The ECG signal, which represents the electrical activity of the human heart, consists of distinctive components such as the P wave, QRS complex, and T wave. Manually annotating these regions is a time - consuming task, necessitating the exploration of automated approaches. To address this challenge, a Long Short-Term Memory (LSTM) network is employed and trained on a dataset sourced from the publicly available QT Database. An automated expert system created the signal area labels for the dataset, which consists of ECG recordings from several patients. The work uses time - frequency analysis and signal pre -processing methods, notably the Fourier synchrosqueezed transform, to capture the nonstationary pr operties of the ECG signal. By combining deep learning with signal processing, the proposed methodology aims to enhance the accuracy and efficiency of waveform segmentation in ECG signals. This research contributes to streamlining the analysis of ECG data, enabling rapid assessment of heart health and the identification of abnormalities.