An Efficient R-peak Detection Based on New Nonlinear Transformation and First-Order Gaussian Differentiator

Abstract

In this paper, we present a reliable and efficient automatic R-wave detection based on new nonlinear transformation and simple peak-finding strategy. The detection algorithm consists of four stages. In the first stage, the bandpass filtering and differentiation operations are used to enhance QRS complexes and to reduce out-of-band noise. In the second stage, we introduce a new nonlinear transformation based on energy thresholding, Shannon energy computation, and smoothing processes to obtain a positive-valued feature signal which includes large candidate peaks corresponding to the QRS complex regions. The energy thresholding reduces the effect of spurious noise spikes from muscle artifacts. The Shannon energy transformation amplifies medium amplitudes and results in small deviations between successive peaks. Therefore, the proposed nonlinear transformation is capable of reducing the number of false-positives and false-negatives under small-QRS and wide-QRS complexes and noisy ECG signals. In the third stage, we propose a simple peak-finding strategy based on the first-order Gaussian differentiator (FOGD) that accurately identifies locations of candidate R-peaks in a feature signal. This stage computes convolution of the smooth feature signal and FOGD operator. The resultant convolution output has negative zero-crossings (ZCs) around the candidate peaks of feature signal due to the anti-symmetric nature of the FOGD operator. Thus, these negative ZCS are detected and used as guides to find locations of real R-peaks in an original signal at the fourth stage. Unlike other existing algorithms, the proposed algorithm does not use search back algorithm and learning phase. The proposed algorithm is validated using the standard MIT-BIH arrhythmia database and achieves an average sensitivity of 99.94% and a positive predictivity of 99.96%. Experimental results show that the proposed algorithm outperforms other existing algorithms in case of different QRS complex morphologies (negative, low-amplitude, wide), very big change in amplitudes of adjacent R-peaks, irregular heart rates, and noisy ECG signals.