Analysis of Heart Rate Variability in Normal and Diabetic ECG signals using Fragmentation Approach.

In this work, an attempt is made to quantify the dynamics of the heart rate variability timeseries in normal and diabetic population using fragmentation metrics. ECG signals recorded during deep breathing and head tilt up experiments are utilized for this study. The QRS-wave of ECG is extracted using the Pan Tompkins Algorithm. Heart rate variability features such as heart rate, Percentage of Inflection Points (PIP) and Inverse of the Average Length of the acceleration/deceleration Segment (IALS) are extracted to quantify the variation in signal dynamics. The results indicate that the ECG signals and heart rate variability signals obtained in deep breathing and tilt exhibit varied characteristics in both normal and diabetics. Further, in the diabetic condition the fragmentation measures exhibit a higher value in both deep breathing and tilt which indicates increased alternations in the signal. Most of the extracted fragmentation features are statistically significant (p<0.005) in differentiating normal and diabetic population. It appears that this method of analysis has potential towards the development of systems for the noninvasive assessment of diabetes.Clinical Relevance- This establishes a technique to quantify the variation in cardiovascular dynamics in normal and diabetic population.

Analysis of Facial Electromyography Signals Using Linear and Non-Linear Features for Human-Machine Interface.

In this work, an attempt has been made to analyze the facial electromyography (facial EMG) signals using linear and non-linear features for the human-machine interface. Facial EMG signals are obtained from the publicly available, widely used DEAP dataset. Thirty-two healthy subjects volunteered for the establishment of this dataset. The signals of one positive emotion (joy) and one negative emotion (sadness) obtained from the dataset are used for this study. The signals are segmented into 12 epochs of 5 seconds each. Features such as sample entropy and root mean square (RMS) are extracted from each epoch for analysis. The results indicate that facial EMG signals exhibit distinct variations in each emotional stimulus. The statistical test performed indicates statistical significance (p<0.05) in various epochs. It appears that this method of analysis could be used for developing human-machine interfaces, especially for patients with severe motor disabilities such as people with tetraplegia.