SB: Conceptualization, Methodology, Software, Data curation, Investigation, Formal analysis, Writing—original draft. ISM: Conceptualization, Supervision, Writing—review & editing. KNS: Validation, Resources, Investigation. The authors declare that this manuscript is original, has not been published previously, and is not under consideration for publication elsewhere. All authors have read and approved the final manuscript and agree to its submission.
Conflicts of interest
The authors declare that they don’t have conflict of interest that could have appeared to influence the work reported in this paper.
Ethical approval
This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the institutional Ethics Committee (IEC) of Jadavpur University (Approval No. IEC/73/C/26).
Consent to participate
Received the written consent was obtained from the patients whose PCG signals were collected for validation of the model, and such consent can be provided on request.
Open Exploration maintains a neutral stance on jurisdictional claims in published institutional affiliations and maps. All opinions expressed in this article are the personal views of the author(s) and do not represent the stance of the editorial team or the publisher.
References
Ghosh SK, Tripathy RK, Ponnalagu RN, Pachori RB. Automated Detection of Heart Valve Disorders From the PCG Signal Using Time-Frequency Magnitude and Phase Features.IEEE Sens Lett. 2019;3:1–4. [DOI]
Li Z, Deng Y, Xiang B, Fu Y. Global, Regional, and National Burden of Valvular Heart Disease, 1990-2021: Analysis of Data from the Global Burden of Disease Study 2021.SSRN Electron J Art. 2024:4919856. [DOI]
Manjunath CN, Srinivas P, Ravindranath KS, Dhanalakshmi C. Incidence and patterns of valvular heart disease in a tertiary care high-volume cardiac center: A single center experience.Indian Heart J. 2014;66:320–6. [DOI] [PubMed] [PMC]
Coffey S, Cairns BJ, Iung B. The modern epidemiology of heart valve disease.Heart. 2015;102:75–85. [DOI] [PubMed]
Coffey S, Roberts-Thomson R, Brown A, Carapetis J, Chen M, Enriquez-Sarano M, et al. Global epidemiology of valvular heart disease.Nat Rev Cardiol. 2021;18:853–64. [DOI] [PubMed]
Michelena HI, Prakash SK, Della Corte A, Bissell MM, Anavekar N, Mathieu P, et al. Bicuspid aortic valve: Identifying knowledge gaps and rising to the challenge from the International Bicuspid Aortic Valve Consortium (BAVCon).Circulation. 2014;129:2691–704. [DOI] [PubMed] [PMC]
Carapetis JR, Steer AC, Mulholland EK, Weber M. The global burden of group A streptococcal diseases.Lancet Infect Dis. 2005;5:685–94. [DOI] [PubMed]
Rothenbühler M, O'Sullivan CJ, Stortecky S, Stefanini GG, Spitzer E, Estill J, et al. Active surveillance for rheumatic heart disease in endemic regions: a systematic review and meta-analysis of prevalence among children and adolescents.Lancet Glob Health. 2014;2:e717–26. [DOI] [PubMed]
Karhade J, Dash S, Ghosh SK, Dash DK, Tripathy RK. Time–Frequency-Domain Deep Learning Framework for the Automated Detection of Heart Valve Disorders Using PCG Signals.IEEE Trans Instrum Meas. 2022;71:1–11. [DOI]
Borer JS, Sharma A. Drug Therapy for Heart Valve Diseases.Circulation. 2015;132:1038–45. [DOI] [PubMed] [PMC]
Praz F, Borger MA, Lanz J, Marin-Cuartas M, Abreu A, Adamo M, et al. 2025 ESC/EACTS guidelines for the management of valvular heart disease.Eur Heart J. 2025;46:4635–736. [DOI] [PubMed]
Leng S, Tan RS, Chai KTC, Wang C, Ghista D, Zhong L. The electronic stethoscope.BioMed Eng OnLine. 2015;14:e14. [DOI]
Luo H, Lamata P, Bazin S, Bautista T, Barclay N, Shahmohammadi M, et al. Smartphone as an electronic stethoscope: factors influencing heart sound quality.Eur Heart J - Digit Health. 2022;3:473–80. [DOI] [PubMed] [PMC]
Roy TS, Roy JK, Mandal N, Mukhopadhyay SC. Recent Advances in PCG Signal Analysis using AI: A Review.Int J Smart Sens Intell Syst. 2024;17:e17. [DOI]
Deperlioglu O, Kose U, Gupta D, Khanna A, Sangaiah AK. Diagnosis of heart diseases by a secure Internet of Health Things system based on Autoencoder Deep Neural Network.Comput Commun. 2020;162:31–50. [DOI] [PubMed] [PMC]
Deng M, Meng T, Cao J, Wang S, Zhang J, Fan H. Heart sound classification based on improved MFCC features and convolutional recurrent neural networks.Neural Netw. 2020;130:22–32. [DOI] [PubMed]
Krishnan PT, Balasubramanian P, Umapathy S. Automated heart sound classification system from unsegmented phonocardiogram (PCG) using deep neural network.Phys Eng Sci Med. 2020;43:505–15. [DOI] [PubMed]
Noman F, Ting C, Salleh S, Ombao H. Short-segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks.ICASSP 2019 - 2019 IEEE Int Conf Acoust Speech Signal Process (ICASSP). 2019:1318–22. [DOI]
Tariq Z, Shah SK, Lee Y. Feature-Based Fusion Using CNN for Lung and Heart Sound Classification.Sensors. 2022;22:1521. [DOI] [PubMed] [PMC]
Arora V, Leekha R, Singh R, Chana I. Heart sound classification using machine learning and phonocardiogram.Mod Phys Lett B. 2019;33:1950321. [DOI]
Milani M, Abas PE, De Silva LC, Nanayakkara ND. Abnormal heart sound classification using phonocardiography signals.Smart Health. 2021;21:100194. [DOI]
Oh SL, Jahmunah V, Ooi CP, Tan RS, Ciaccio EJ, Yamakawa T, et al. Classification of heart sound signals using a novel deep WaveNet model.Comput Methods Programs Biomed. 2020;196:105604. [DOI] [PubMed]
Li F, Liu M, Zhao Y, Kong L, Dong L, Liu X, et al. Feature extraction and classification of heart sound using 1D convolutional neural networks.EURASIP J Adv Signal Process. 2019;2019:e2019. [DOI]
Alkhodari M, Fraiwan L. Convolutional and recurrent neural networks for the detection of valvular heart diseases in phonocardiogram recordings.Comput Methods Programs Biomed. 2021;200:105940. [DOI] [PubMed]
Barua PD, Karasu M, Kobat MA, Balık Y, Kivrak T, Baygin M, et al. An accurate valvular heart disorders detection model based on a new dual symmetric tree pattern using stethoscope sounds.Comput Biol Med. 2022;146:105599. [DOI] [PubMed]
Nabih-Ali M, El-Dahshan EA, Yahia AS. A review of intelligent systems for heart sound signal analysis.J Med Eng Technol. 2017;41:553–63. [DOI] [PubMed]
Nabhan Homsi M, Warrick P. Ensemble methods with outliers for phonocardiogram classification.Physiol Meas. 2017;38:1631–44. [DOI] [PubMed]
Baghel N, Dutta MK, Burget R. Automatic diagnosis of multiple cardiac diseases from PCG signals using convolutional neural network.Comput Methods Programs Biomed. 2020;197:105750. [DOI] [PubMed]
Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, et al. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning.IEEE Trans Med Imaging. 2016;35:1285–98. [DOI] [PubMed] [PMC]
Dong Y, Jiang Z, Shen H, David Pan W, Williams LA, Reddy VVB, et al. Evaluations of deep convolutional neural networks for automatic identification of malaria infected cells.2017 IEEE EMBS Int Conf Biomed Health Inform (BHI). 2017:101–4. [DOI]
Fahad HM, Ghani Khan MU, Saba T, Rehman A, Iqbal S. Microscopic abnormality classification of cardiac murmurs using ANFIS and HMM.Microsc Res Tech. 2018;81:449–57. [DOI] [PubMed]
Chen J, Guo Z, Xu X, Jeon G, Camacho D. Artificial intelligence for heart sound classification: A review.Expert Syst. 2024;41:e41. [DOI]
Zhang W, Han J, Deng S. Heart sound classification based on scaled spectrogram and tensor decomposition.Expert Syst Appl. 2017;84:220–31. [DOI]
Zheng Y, Guo X, Qin J, Xiao S. Computer-assisted diagnosis for chronic heart failure by the analysis of their cardiac reserve and heart sound characteristics.Comput Methods Programs Biomed. 2015;122:372–83. [DOI] [PubMed]
Maity A, Misra IS. Prototype Design of An IoT Enabled Cost-Efficient Portable Heart-Health Data Acquisition System.2020 IEEE Calcutta Conf (CALCON). 2020:137–41. [DOI]
Rouis M, Sbaa S, Benhassine NE. The effectiveness of the choice of criteria on the stationary and non-stationary noise removal in the phonocardiogram (PCG) signal using discrete wavelet transform.Biomed Eng / Biomed Tech. 2019;65:353–66. [DOI] [PubMed]
GHOSH SK, PONNALAGU RN. INVESTIGATION OF DISCRETE WAVELET TRANSFORM DOMAIN OPTIMAL PARAMETRIC APPROACH FOR DENOISING OF PHONOCARDIOGRAM SIGNAL.J Mech Med Biol. 2022;22:2250046. [DOI]
Qazi EUH, Almorjan A, Zia T. A One-Dimensional Convolutional Neural Network (1D-CNN) Based Deep Learning System for Network Intrusion Detection.Appl Sci. 2022;12:7986. [DOI]
Kiranyaz S, Avci O, Abdeljaber O, Ince T, Gabbouj M, Inman DJ. 1D convolutional neural networks and applications: A survey.Mech Syst Signal Process. 2021;151:107398. [DOI]
Younesi A, Ansari M, Fazli M, Ejlali A, Shafique M, Henkel J. A Comprehensive Survey of Convolutions in Deep Learning: Applications, Challenges, and Future Trends.IEEE Access. 2024;12:41180–218. [DOI]
