Abstract

Automated ECG signal processing can assist in diagnosing several heart diseases. Many R peak detection methods have been studied because the accuracy of R peak detection significantly affects the quality of subsequent ECG feature extraction. Two important steps in R peak detection algorithm that draw attention over researchers are the preprocessing and thresholding stages. Among several methods, wavelet transform is a widely used method for removing noise in the preprocessing stage. Various proposed algorithms require prior knowledge of frequency spectrum of the signal under consideration in order to select the wavelet detail coefficients in the reconstruction process. Moreover, parameter fine tuning is generally involved in threshold selection to accomplish high detection accuracy. As a result, it may be difficult to utilize these methods for general ECG data sets. Accordingly, we propose an automatic and parameter free method that optimally selects the appropriate detail components for wavelet reconstruction as well as the adaptive threshold. The proposed algorithm employs the analysis of probability density function of the processed ECG signal. The validation of the algorithm was performed over the MIT-BIH database and has produced an average sensitivity of 99.63% and specificity of 99.78% which is in the same range as the previously proposed approaches.