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Article

Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features

1
Department of Electronics Engineering, University of Engineering and Technology Taxila, Taxila 47080, Pakistan
2
College of Computer Science and Engineering, University of Ha’il, Ha’il 55211, Saudi Arabia
3
Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Campus, Wah Cantonment 47040, Pakistan
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2020, 20(13), 3790; https://doi.org/10.3390/s20133790
Received: 25 May 2020 / Revised: 30 June 2020 / Accepted: 1 July 2020 / Published: 6 July 2020
(This article belongs to the Special Issue Signal Processing Using Non-invasive Physiological Sensors)
Congenital heart disease (CHD) is a heart disorder associated with the devastating indications that result in increased mortality, increased morbidity, increased healthcare expenditure, and decreased quality of life. Ventricular Septal Defects (VSDs) and Arterial Septal Defects (ASDs) are the most common types of CHD. CHDs can be controlled before reaching a serious phase with an early diagnosis. The phonocardiogram (PCG) or heart sound auscultation is a simple and non-invasive technique that may reveal obvious variations of different CHDs. Diagnosis based on heart sounds is difficult and requires a high level of medical training and skills due to human hearing limitations and the non-stationary nature of PCGs. An automated computer-aided system may boost the diagnostic objectivity and consistency of PCG signals in the detection of CHDs. The objective of this research was to assess the effects of various pattern recognition modalities for the design of an automated system that effectively differentiates normal, ASD, and VSD categories using short term PCG time series. The proposed model in this study adopts three-stage processing: pre-processing, feature extraction, and classification. Empirical mode decomposition (EMD) was used to denoise the raw PCG signals acquired from subjects. One-dimensional local ternary patterns (1D-LTPs) and Mel-frequency cepstral coefficients (MFCCs) were extracted from the denoised PCG signal for precise representation of data from different classes. In the final stage, the fused feature vector of 1D-LTPs and MFCCs was fed to the support vector machine (SVM) classifier using 10-fold cross-validation. The PCG signals were acquired from the subjects admitted to local hospitals and classified by applying various experiments. The proposed methodology achieves a mean accuracy of 95.24% in classifying ASD, VSD, and normal subjects. The proposed model can be put into practice and serve as a second opinion for cardiologists by providing more objective and faster interpretations of PCG signals. View Full-Text
Keywords: phonocardiogram; machine learning; empirical mode decomposition; feature extraction; mel-frequency cepstral coefficients; support vector machines; computer aided diagnosis; congenital heart disease; statistical analysis phonocardiogram; machine learning; empirical mode decomposition; feature extraction; mel-frequency cepstral coefficients; support vector machines; computer aided diagnosis; congenital heart disease; statistical analysis
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MDPI and ACS Style

Aziz, S.; Khan, M.U.; Alhaisoni, M.; Akram, T.; Altaf, M. Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features. Sensors 2020, 20, 3790. https://doi.org/10.3390/s20133790

AMA Style

Aziz S, Khan MU, Alhaisoni M, Akram T, Altaf M. Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features. Sensors. 2020; 20(13):3790. https://doi.org/10.3390/s20133790

Chicago/Turabian Style

Aziz, Sumair, Muhammad U. Khan, Majed Alhaisoni, Tallha Akram, and Muhammad Altaf. 2020. "Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features" Sensors 20, no. 13: 3790. https://doi.org/10.3390/s20133790

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