Design and implementation of a robust noise removal system in ECG signals using dual-tree complex wavelet transform

Abstract The key deliverable for any health monitoring system that offers telecardiology services is the recovery of the ECG signal related to cardiac diagnostics. Accurate analysis and diagnosis of heart diseases become difficult due to noises and artifacts. Therefore, a need for noise removal which is an inverse problem arises that leads to reliable signal recovery tasks. In this paper, authors have provided a detailed evaluation of the effect of the choice of the threshold value, threshold algorithm, and distribution function to evaluate the ECG signal de-noising performance employing Dual tree complex wavelet transform (DTCWT). In this research work, eight different sets of threshold value selection rules along with six distinct threshold functions are implemented and evaluated on MIT-BIH arrhythmia database. Authors have proposed an estimator that yields efficient results than a conventional estimator using Gaussian distribution function and extended the work to Normal distribution. The results show that the Universal modified threshold level-dependent threshold value selection with Non -Negative Garrote threshold function delivers higher performance in terms of SNR, lower values of MSE and PRD. The proposed approach accomplished good performance evaluation results as 58.23 dB, SNR and 9.63e-08, MSE in comparison with the 48.65 dB, SNR and 8.75e-07, MSE using conventional method for the normal distribution function. The novelty of this work also lies in an exhaustive empirical comparative analysis with the existing research work using the same database with the proposed normal distribution technique.