PCA based R-peak detection in ECG signal with ABO

A novel approach for R-peak detection in Electrocardiogram (ECG) signal is proposed by combining Principal Component Analysis (PCA) with advanced signal processing techniques, including Discrete Wavelet Transform (DWT), Continuous Wavelet Transform (CWT), and optimization via African Buffalo Optimization (ABO). First, PCA is applied to reduce the dimensionality of the ECG signal, enhancing the most relevant features for R-peak detection. The signal is then decomposed using DWT and CWT to capture both high and low-frequency components, enabling effective identification of R-wave characteristics. To improve the detection accuracy, ABO is utilized to optimize the thresholds and parameters of the wavelet transforms. The proposed method is evaluated on publicly available ECG datasets, demonstrating superior performance in terms of detection accuracy (DA), sensitivity (Se), specificity (Sp) and F1 score when compared to traditional R-peak detection algorithms.