Login Register Cart 0
Generic placeholder image

Current Signal Transduction Therapy

Editor-in-Chief

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

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
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

Abstract

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

[1]
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.
[http://dx.doi.org/10.1186/s13634-018-0545-9]
[2]
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.
[http://dx.doi.org/10.1109/10.277274] [PMID: 8200671]
[3]
Debbal SM, Bereksi-Reguig F. Computerized heart sounds analysis. Comput Biol Med 2008; 38(2): 263-80.
[http://dx.doi.org/10.1016/j.compbiomed.2007.09.006] [PMID: 18037395]
[4]
Yadav D, Yadav S, Veer K. Trends and applications of brain computer interfaces. Curr Signal Transduct Ther 2020; (16): 211-23.
[5]
Pooja SK, Pahuja SK, Veer K. Recent approaches on classification and feature extraction of EEG signal: A review. Robotica 2022; 40(1): 77-101.
[http://dx.doi.org/10.1017/S0263574721000382]
[6]
Yadav D, Yadav S, Veer K. A comprehensive assessment of brain computer interfaces: Recent trends and challenges. J Neurosci Methods 2020; 346(346): 108918.
[http://dx.doi.org/10.1016/j.jneumeth.2020.108918] [PMID: 32853592]
[7]
Veer K. Spectral and mathematical evaluation of electromyography signals for clinical use. Int J Biomath 2016; 9(6): 1650094.
[http://dx.doi.org/10.1142/S1793524516500947]
[8]
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.
[9]
Goda MA, Péter H. Morphological determination of pathological PCG signals by time and frequency domain analysis 2016 computing in cardiology conference (CinC). IEEE 2016.
[http://dx.doi.org/10.22489/CinC.2016.324-249]
[10]
Abdollahpur M. Cycle selection and neuro-voting system for classifying heart sound recordings. Comput Cardiol 2016; (43): 176-238.
[11]
Abo-Zahhad Mohammed M. A comparative approach between cepstral features for human authentication using heart sounds. SIViP 2011; 10: 843-51.
[12]
Kao WC, Wei CC. Automatic phonocardiograph signal analysis for detecting heart valve disorders. Expert Syst Appl 2011; 38(6): 6458-68.
[http://dx.doi.org/10.1016/j.eswa.2010.11.100]
[13]
Hotelling H. Analysis of a complex of statistical variables into principal components. J Educ Psychol 1933; 24(6): 417-41.
[http://dx.doi.org/10.1037/h0071325]
[14]
Jackson JE. A user’s guide to principal components. New York, NY: Wiley 1991.
[http://dx.doi.org/10.1002/0471725331]
[15]
Veer K. A flexible approach for segregating physiological signals. Measurement 2016; 87(87): 21-6.
[http://dx.doi.org/10.1016/j.measurement.2016.03.017]
[16]
Jolliffe IT. Principal component analysis. (2nd ed.), New York, NY: Springer-Verlag 2002.
[17]
Diamantaras KI, Kung SY. Principal component neural networks: Theory and applications. New York, NY: Wiley 1996.
[18]
Flury B. Common principal components and related models. New York, NY: Wiley 1988.
[19]
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.
[20]
Jolliffe IT, Cadima J. Principal component analysis: A review and recent developments. Philos Trans-A Math Phys Eng Sci 2016; 374(2065): 20150202.
[http://dx.doi.org/10.1098/rsta.2015.0202] [PMID: 26953178]

Rights & Permissions Print Cite
Article Metrics
15
2
© 2024 Bentham Science Publishers | Privacy Policy