Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features
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 c...
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| Format: | Article |
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MDPI AG
2020-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/13/3790 |
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| author | Sumair Aziz Muhammad Umar Khan Majed Alhaisoni Tallha Akram Muhammad Altaf |
| author_facet | Sumair Aziz Muhammad Umar Khan Majed Alhaisoni Tallha Akram Muhammad Altaf |
| author_sort | Sumair Aziz |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T18:39:22Z |
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| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T18:39:22Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-48bd0c1c4fa7480ebc96347b8997f3462023-11-20T06:00:10ZengMDPI AGSensors1424-82202020-07-012013379010.3390/s20133790Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral FeaturesSumair Aziz0Muhammad Umar Khan1Majed Alhaisoni2Tallha Akram3Muhammad Altaf4Department of Electronics Engineering, University of Engineering and Technology Taxila, Taxila 47080, PakistanDepartment of Electronics Engineering, University of Engineering and Technology Taxila, Taxila 47080, PakistanCollege of Computer Science and Engineering, University of Ha’il, Ha’il 55211, Saudi ArabiaDepartment of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Campus, Wah Cantonment 47040, PakistanDepartment of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Campus, Wah Cantonment 47040, PakistanCongenital 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.https://www.mdpi.com/1424-8220/20/13/3790phonocardiogrammachine learningempirical mode decompositionfeature extractionmel-frequency cepstral coefficientssupport vector machines |
| spellingShingle | Sumair Aziz Muhammad Umar Khan Majed Alhaisoni Tallha Akram Muhammad Altaf Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features Sensors phonocardiogram machine learning empirical mode decomposition feature extraction mel-frequency cepstral coefficients support vector machines |
| title | Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features |
| title_full | Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features |
| title_fullStr | Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features |
| title_full_unstemmed | Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features |
| title_short | Phonocardiogram Signal Processing for Automatic Diagnosis of Congenital Heart Disorders through Fusion of Temporal and Cepstral Features |
| title_sort | phonocardiogram signal processing for automatic diagnosis of congenital heart disorders through fusion of temporal and cepstral features |
| topic | phonocardiogram machine learning empirical mode decomposition feature extraction mel-frequency cepstral coefficients support vector machines |
| url | https://www.mdpi.com/1424-8220/20/13/3790 |
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