Generic placeholder image

Current Signal Transduction Therapy


ISSN (Print): 1574-3624
ISSN (Online): 2212-389X

Research Article

Interpretation and Classification of Phonocardiogram Using Principal Component Analysis

Author(s): Nikita Jatia, Sachin Kumar and Karan Veer*

Volume 18, Issue 2, 2023

Published on: 09 August, 2023

Article ID: e030823219411 Pages: 6

DOI: 10.2174/1574362418666230803145322

Price: $65


Background: Large datasets are logically common yet frequently difficult to interpret. Principal Component Analysis (PCA) is a technique to reduce the dimensionality of a dataset.

Objective: The main objective of this work is to use principal component analysis to interpret and classify phonocardiogram signals.

Methods: 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.

Results: 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.

Conclusion: Long-term observation is difficult due to the many complexities, such as the lack of human competence and the high chance of misdiagnosis.

Keywords: Principal Component Analysis (PCA), phonocardiogram (PCG), data examination technique, dimensionality reduction, phonocardiogram signals, misdiagnosis.

Graphical Abstract
Ismail S, Siddiqi I, Akram U. Localization and classification of heart beats in phonocardiography signals-A comprehensive review. EURASIP J Adv Signal Process 2018; 2018(1): 26.
Bertrand O, Bohorquez J, Pernier J. Time-frequency digital filtering based on an invertible wavelet transform: An application to evoked potentials. IEEE Trans Biomed Eng 1994; 41(1): 77-88.
[] [PMID: 8200671]
Debbal SM, Bereksi-Reguig F. Computerized heart sounds analysis. Comput Biol Med 2008; 38(2): 263-80.
[] [PMID: 18037395]
Yadav D, Yadav S, Veer K. Trends and applications of brain computer interfaces. Curr Signal Transduct Ther 2020; (16): 211-23.
Pooja SK, Pahuja SK, Veer K. Recent approaches on classification and feature extraction of EEG signal: A review. Robotica 2022; 40(1): 77-101.
Yadav D, Yadav S, Veer K. A comprehensive assessment of brain computer interfaces: Recent trends and challenges. J Neurosci Methods 2020; 346(346): 108918.
[] [PMID: 32853592]
Veer K. Spectral and mathematical evaluation of electromyography signals for clinical use. Int J Biomath 2016; 9(6): 1650094.
Veer K. Wavelet transform to recognize muscular: Force relationship using sEMG signals. Proceedings of the national academy of sciences, India Section A: Physical Sciences. 86: 103-12.
Goda MA, Péter H. Morphological determination of pathological PCG signals by time and frequency domain analysis 2016 computing in cardiology conference (CinC). IEEE 2016.
Abdollahpur M. Cycle selection and neuro-voting system for classifying heart sound recordings. Comput Cardiol 2016; (43): 176-238.
Abo-Zahhad Mohammed M. A comparative approach between cepstral features for human authentication using heart sounds. SIViP 2011; 10: 843-51.
Kao WC, Wei CC. Automatic phonocardiograph signal analysis for detecting heart valve disorders. Expert Syst Appl 2011; 38(6): 6458-68.
Hotelling H. Analysis of a complex of statistical variables into principal components. J Educ Psychol 1933; 24(6): 417-41.
Jackson JE. A user’s guide to principal components. New York, NY: Wiley 1991.
Veer K. A flexible approach for segregating physiological signals. Measurement 2016; 87(87): 21-6.
Jolliffe IT. Principal component analysis. (2nd ed.), New York, NY: Springer-Verlag 2002.
Diamantaras KI, Kung SY. Principal component neural networks: Theory and applications. New York, NY: Wiley 1996.
Flury B. Common principal components and related models. New York, NY: Wiley 1988.
Karan V. A technique for classification and decomposition of muscle signal for control of myoelectric prostheses based on wavelet statistical classifier. Measurement 2015; (60): 283-91.
Jolliffe IT, Cadima J. Principal component analysis: A review and recent developments. Philos Trans-A Math Phys Eng Sci 2016; 374(2065): 20150202.
[] [PMID: 26953178]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy