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In this paper, Emotional recognition in Parkinson's disease (PD) has been analyzed in both time and frequency domain using Entropy, Energy-Entropy and Teager Energy-Entropy features. Classification results were observed using three classifiers namely Probabilistic Neural Network, K-Nearest Neighbors Algorithm and Support Vector Machine. Emotional EEG stimuli such as happiness, sadness, fear, anger, surprise, and disgust were used to categorize the PD patients and healthy controls (HC). For each EEG signal, the alpha, beta and gamma band frequency features are obtained for six different feature extraction methods (Entropy, Energy-Entropy, Teager Energy-Entropy, Spectral Entropy, Spectral Energy-Entropy and Spectral Teager Energy-Entropy). The proposed Energy-Entropy feature performs evenly for all six emotions with accuracy of above 80% when compared to other features, whereas different features with classifier gives variant results for few emotions with highest accuracy of above 95%. [ABSTRACT FROM AUTHOR]

The survival rate of preterm infants has been increased by the development in treatment methods, still premature birth increases globally. These infants are at risk of medical condition that affects the respiratory, gastrointestinal, immune, central nervous, auditory and visual systems. Better understanding of preterm birth can be utilized for the prevention of such birth. This paper explores the significance of entropy features and classification using machine learning approach. Classification of spectral energy entropy feature using Elman neural network shows better result with specificity 99.85%, sensitivity 99.92% and accuracy of 99.8875%. [ABSTRACT FROM AUTHOR]