CWT and LDA Based ECG Signal Analysis for R-Peak Detection

The present work presents a novel approach for detecting R-peaks in electrocardiogram (ECG) signals by combining Continuous Wavelet Transform (CWT) and Linear Discriminant Analysis (LDA). Accurate detection of R-peaks is crucial for various clinical applications such as heart rate monitoring and arrhythmia detection. The proposed method utilizes CWT for feature extraction, which provides a time-frequency representation of the ECG signal, enabling better resolution of the signal's transient features. The extracted features are then processed using LDA, a reduction technique to enhance the discriminative power and distinguish R-peaks from other components of the ECG. Experimental results demonstrate that the combination of CWT and LDA significantly improves R-peak detection accuracy compared to traditional methods. In this work the performance of proposed work is judged on the basis of recall value (Re) and accuracy (Ac). The proposed approach achieves robust performance in different signal conditions, making it a promising solution for real-time ECG analysis and heart health monitoring systems.