Abstract:
Researchers have found that the distinctiveness of the phonocardiogram (PCG) signal can be used
for biometric identification, unlike advances in heart sound analysis for screening and diagnosing
different heart problems. Biometrics, functioning as a measure and statistical test, proves
invaluable for representing the distinctive behavioral and intellectual characteristics of individuals.
Auscultation through a stethoscope is a non-invasive, and cost-effective technique for heart sound
analysis in both diagnostic and identification applications. However, the non-stationary nature of
heart sounds renders exclusive spectral or time domain analysis ineffective. That's why it's
important to make the transition to time-frequency analysis.
This study introduces a method for heart sound analysis aimed at identifying subjects based on the
uniqueness of their heart sounds. An electronic stethoscope was developed for signal acquisition,
560 heart sound recordings were obtained from 20 participants, At a uniform sampling frequency
of 44.1 KHz and 16-bit depth. For machine learning based classification, data augmentation was
performed using amplitude scaling techniques within a scaling range of [0.8, 1.2]. Discrete
Wavelet Transform (DWT) based denoising of the raw PCG signal employed a Db10 wavelet
family at 5th level of decomposition and soft thresholding method. Feature extraction utilized
DWT-based and Mel-Frequency Cepstral Coefficients (MFCC) techniques, followed by the
application of a feature reduction algorithm to eliminate irrelevant features. The selected features
underwent training using a cubic Support Vector Machine (SVM), chosen for its superior
classification accuracy among various machine learning classifiers. Bayesian optimization was
applied during the training phase.
System performance was evaluated on an independent dataset during the testing phase, resulting
in a notable accuracy, precision, recall, and F1 score of 96.6%, 96.7%, 96.7%, and 96.6%,
respectively. The proposed method demonstrates significant enhancement in representing distinct
features crucial for identity recognition. As a future extension, the method will be refined using a
larger dataset on heart sound to enhance its further capabilities on classification.
