Interpretation and Classification of Phonocardiogram Using Principal Component Analysis

Large datasets are logically common yet frequently difficult to interpret. Principal Component Analysis (PCA) is a technique to reduce the dimensionality of a dataset. The main objective of this work is to use principal component analysis to interpret and classify phonocardiogram signals. Finding new factors aids in the reduction of important components of an eigenvalue/eigenvector problem, thus enabling the new factors to be represented by the current dataset and making PCA a flexible data analysis tool. PCA is adaptable to a variety of systems created to update different data types and technology advancements. Signals acquired from a patient, i.e., bio-signals, are used to investigate the patient's strength. One such bio-signal of central significance is the phonocardiogram (PCG), which addresses the working of the heart. Any change in the PCG signal is a characteristic proportion of heart failure, an arrhythmia condition. Long-term observation is difficult due to the many complexities, such as the lack of human competence and the high chance of misdiagnosis.