Abstract—Digital volume pulse(DVP) refers to the physiological signal that quantifies the changes in blood volume in the artery during breathing. DVP signals are acquired using methods such as invasive catheterization, mechanical tonometry and photoplethysmography. From the DVP signals critical biological parameters such as heart rate, stiffness index, reflectivity index and pulse wave velocity can be computed. These parameters have shown promise in detecting the early onset of cardiovascular disease (CVD). Thus it is critical that these parameters should be estimated with utmost precision. However DVP signals are corrupted with artifacts due to improper mounting of the sensor, power line interference and other random noises in environment. These artifacts would lead to incorrect estimation of the aforementioned parameters. In this paper the authors evaluate the performance of state of the art algorithms for denoising DVP signals. Denoising using wavelet transforms, empirical mode decomposition, adaptive filters, morphological filters, anisotropic diffusion, total variation denoising and non local means algorithm has been considered in our work. Metrics: mean squared error(MSE), mean absolute error(MAE), signal to noise ratio(SNR), peak signal to noise ratio (PSNR), cross correlation and central processing unit(CPU) consumption time have been computed to assess the performance of each of the methods. From our study, it is concluded that multivariate wavelet denoising yields the best performance and is hence the most suitable method for enhancement of DVP signals.
 

Index Terms—Digital volume pulse (DVP), Denoising, Wavelet transform, Multivariate wavelet denoising, Empirical mode decomposition (EMD), White gaussian noise (WGN)

Cite: Aditya Sundar, Vivek Pahwa, Chinmay Das, Mukund Deshmukh, and Neethu Robinson, "A Comprehensive Assessment of the Performance of Modern Algorithms for Enhancement of Digital Volume Pulse Signals," International Journal of Pharma Medicine and Biological Sciences, Vol. 5, No. 1, pp. 91-98, January 2016. doi: 10.18178/ijpmbs.5.1.91-98