Key result
Adding coupling features to machine learning classifiers, particularly Artificial Neural Networks, improved the prediction efficiency for classifying aging changes in physiological signals.
Why the study?
Physiological signals like HR, BP, and RESP interact through linear and nonlinear relationships, prompting the use of conditional transfer entropy and machine learning classifiers to study aging-related coupling changes.
Adding coupling features based on conditional transfer entropy improves the performance of machine learning classifiers in detecting aging changes from physiological signals.
Conditional transfer entropy features may enhance ML detection of cardiorespiratory coupling; leaves open clinical validation and outcome impact.
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.
No takes yet. Share an insight, caveat, or question.
Singh et al. (2022) studied Healthy young and old subjects. Machine learning classification with coupling features vs. Machine learning classification without coupling features was evaluated on Classification accuracy, sensitivity, and specificity. Adding coupling features to machine learning classifiers, particularly Artificial Neural Networks, improved the prediction efficiency for classifying aging changes in physiological signals.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: