In order to measure the mortality in the patients suffering from the heart disease we use the term HRV that i.e. Heart Rate Variability. Estimation methods as Parametric and Non-Parametric are used in the analysis of Heart Rate Variability but Heart Rate Variability requires the specific capabilities which are not provided by either of these. The term EMD i.e. Empirical Mode Decomposition adaptively estimates the IMF i.e. Intrinsic Mode Function of the nonlinear and nonstationary signal. The IMF obtained from the EMD is used for the analyses of the HRV latencies of Healthy subjects and of Congestive Heart Failure subjects. In this paper we have considered the 15 Congestive Heart Failure patients, 20 healthy young control patients and 20 healthy old control patients. After finding the IMF from EMD we have calculated the average periods, absolute power, normalised power and cumulative power and concerned plots are drawn for the comparison of the considered subjects. The results obtained shows that the HRV of healthy subjects rises rapidly to its maximum response as compared to the HRV of the pathological subjects. This fact can be used as a promising approach in clinical practise for the screening of specific risk group. General TermsHRV Analysis using EMD, Pathological Detection.

This paper attempts to estimate the probability density function of hand movements by using EMG signals. Several hand grasps generated from different hand movements, we have analyzed Tip and Lateral. Four well known pdf functions for good fitness of test are Log Logistics (3P), Johnson, Dagum (4P) and Burr (4P), that have been tested. The probability density function has been carried out in time domain and FFT domain as well as in DWT domain. It was observed that there are different distributions for different hand movements, which describe the samples most accurately with the movements of hand with respect to two channels; channel 1 and channel 2. In this scenario, channels 1 is placed on upper limb and channel 2 placed on lower limbs as a reference channel. Although, Burr distribution and Log logistic distribution along with that Dagum has been a good fit for most of the data, it is shown in this paper that Non Negative distribution (Dagum (4P) and Burr (4P) distribution) is a better choice for estimating the Tip and Lateral hand movements. General TermsThis method is used for classification purpose. This is the another method of classification of data set in probability density function. This is used as a clinic/ engineering field as a designing of prosthetic arm and hand.