ANALYSIS OF CARDIOVASCULAR, CARDIORESPIRATORY, AND VASCULO- RESPIRATORY SIGNALS USING DIFFERENT MACHINE LEARNING TECHNIQUES

Abstract

Many physiological signals such as heart rate (HR), blood pressure (BP), and respiration (RESP) affect each other, and the inter-relation within and between these signals can be linear or nonlinear. Therefore, this paper’s main aim is to extract the relevant features using the information domain coupling technique based on conditional transfer entropy to detect the nonlinearity and coupling changes between the physiological signals and to classify the database using various machine learning classifiers to study the aging changes in the contribution of HR, BP, and RESP. In the proposed work, the physiological signals, i.e. HR, BP, and RESP, were pre-processed using various filtering methods, then features of physiological signals were extracted using linear and nonlinear techniques. After the pre-processing and extraction of features, the extracted features are classified using machine learning classifiers to classify the physiological signal database to study the aging changes in the contribution of HR, BP, and RESP. The data has been taken from the standard Fantasia database of healthy young and old subjects and self-recorded data of healthy young and old subjects for this study. Naive Bayes (NB), Support vector machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), and Artificial Neural Network (ANN) were trained using five-fold cross-validation on the physiological dataset. It is concluded from the results that by adding the coupling features, the efficiency of the final prediction of the classifier increased from [Formula: see text]% to [Formula: see text]% obtained by LR, [Formula: see text]% to [Formula: see text]% obtained by SVM, [Formula: see text]% to [Formula: see text]% obtained by KNN, [Formula: see text]% to [Formula: see text]% obtained by NB, and [Formula: see text]% to [Formula: see text]% obtained by ANN. The ANN performs well when provided with the coupling features, gives a maximum accuracy of [Formula: see text]% and very high sensitivity of [Formula: see text]% and specificity of [Formula: see text]%, and takes much less computational time, when compared to other machine learning algorithms on same length of database.