Adaptive Wavelet Thresholding for Noise reduction in Electrocardiogram (ECG) Signals

Abstract

In diagnosis of diseases Ultrasonic devices are frequently used by healthcare professionals. The medical imaging devices namely X-ray, CT/MRI and ultrasound are producing abundant images which are used by medical practitioners in the process of diagnosis . The main problem faced by them is the noise introduced due to the consequence of the coherent nature of the wave transmitted. These noises corrupt the image and often lead to incorrect diagnosis. In general, ECG signals affected by noises such as baseline wandering, power line interference, electromagnetic interference and high frequency noises during data acquisition. In the recent paper we have considered the Discrete Wavelet Transform (DWT) based wavelet Denoising have incorporated using different Thresholding techniques to remove major sources of noises from the acquired ECG signals. The experimental results shows the significant reduction of White Gaussian noise and it retains the ECG signal morphology effectively. Different performance measures were considered to select the appropriate wavelet function and Thresholding rule for efficient noise removal methods such as Mean Square Error (MSE),Peak Signal to Noise Ratio (PSNR) and Percentage Root Mean Square Difference (PRD) . The experimental result shows the db wavelet and BayesShrink Thresholding rule is optimal for reducing noise in the real time ECG signals.