Heartbeat Abnormality Detection in Phonocardiogram Signals using Wavelet Time Scattering and Optimized KNN Classification
Abstract
Heart auscultation continues to play an essential role
in heart health diagnosis. However, many places
worldwide have a shortage of suitably qualified
medical practitioner’s adept at this ability. This
highlights the critical need to develop accurate
automated systems for evaluating Phonocardiogram
(PCG) data. PCGs are acoustic recordings that capture
the noises made by the heart during its systolic and
diastolic cycles. To solve this issue, we suggest using
Wavelet Time Scattering with an optimized XGBoost
classifier and K-Nearest Neighbors (KNN) classifier
to detect irregular heartbeats in PCG signals. The
results are promising, as the optimized KNN classifier
obtains an impressive accuracy rate of 92.5% when
combined with five-fold cross-validation, which is
better than XGBoost classifier, which gains 87.93%.
This demonstrates the efficacy of the optimized KNN
in improving the automated interpretation of PCG data
and assisting in the early diagnosis of heart-related
problems.