Noise Cancellation of PPG Signals using Wavelet Transformation

Analysis of Biomedical signals in efficient manner can lead to finding out critical diseases and for the patient treatment it will be helpful. Now a day’s Analysis of bio-signals in the clinical point of view is gaining wide range of application. Photoplethysmography is a non-invasive technique that measures relative blood volume changes in the blood vessels close to the skin. We present the results of analysis of photoplethysmography (PPG) signals having motion artefacts which are as like Gaussian noise in nature. This paper discusses a methodology to analysis the significance of different wavelets application such as db4, bior3.3, coif1, sym2, haar in removing the motion artefacts. We applied the haar wavelet (level-5) for compressing the signals and to derive the residuals of both the input & output filtered signals, so as to study the change in standard deviation and mean abs deviation. The abs mean deviation of input signal was 50.05 and the De-noised PPG signal was found 48.33 with db4 wavelet. In our case the db4 wavelet is showing better result than the other wavelets. De-noised Signal helps to study large artery damage and any abnormality in the cardiovascular disease which is one of the common causes of high mortality rate.