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Automated detection of heart valve diseases using chirplet transform and multiclass composite classifier with PCG
Samit Kumar Ghosh1, R N Ponnalagu1, R K Tripathy1
1Department of Electrical and Electronics Engineering, BITS-Pilani, Hyderabad Campus, Hyderabad 500078, India.
Insights
Early detection of heart valve diseases (HVDs) is crucial. This study introduces a novel Chirplet Transform (CT) method using phonocardiogram (PCG) signals for accurate HVD classification, improving patient outcomes.
Area of Science:
- Cardiovascular Engineering
- Biomedical Signal Processing
- Machine Learning in Healthcare
Background:
- Heart valve diseases (HVDs) pose significant mortality risks if untreated.
- Timely detection of HVDs is critical for effective cardiovascular disease management.
- Phonocardiogram (PCG) signals offer a non-invasive window into heart valve function.
Purpose of the Study:
- To develop and validate a novel approach for the automated detection and classification of HVDs.
- To leverage advanced signal processing techniques for enhanced PCG analysis.
- To improve diagnostic accuracy for conditions like aortic stenosis, mitral stenosis, and mitral regurgitation.
Main Methods:
- Utilized Chirplet Transform (CT) for time-frequency (TF) analysis of PCG signals.
- Extracted local energy (LEN) and local entropy (LENT) features from the TF matrix.
- Employed a multiclass composite classifier based on sparse representation and nearest neighbor distances for HVD classification.
Main Results:
- Achieved high sensitivity: 99.44% for aortic stenosis (AS), 98.66% for mitral stenosis (MS), and 96.22% for mitral regurgitation (MR).
- Demonstrated superior overall accuracy compared to existing methods on the same dataset.
- The proposed CT-based feature extraction method proved effective for automated HVD classification.
Conclusions:
- The proposed Chirplet Transform-based method offers a promising tool for automated HVD detection.
- This approach shows potential for integration into Internet of Medical Things (IOMT) applications for remote cardiovascular monitoring.
- Accurate and early HVD detection can significantly reduce mortality rates and improve patient prognosis.
Abstract:
Heart valve diseases (HVDs) are a group of cardiovascular abnormalities, and the causes of HVDs are blood clots, congestive heart failure, stroke, and sudden cardiac death, if not treated timely. Hence, the detection of HVDs at the initial stage is very important in cardiovascular engineering to reduce the mortality rate. In this article, we propose a new approach for the detection of HVDs using phonocardiogram (PCG) signals. The approach uses the Chirplet transform (CT) for the time-frequency (TF) based analysis of the PCG signal. The local energy (LEN) and local entropy (LENT) features are evaluated from the TF matrix of the PCG signal. The multiclass composite classifier formulated based on the sparse representation of the test PCG instance for each class and the distances from the nearest neighbor PCG instances are used for the classification of HVDs such as mitral regurgitation (MR), mitral stenosis (MS), aortic stenosis (AS), and healthy classes (HC). The experimental results show that the proposed approach has sensitivity values of 99.44%, 98.66%, and 96.22% respectively for AS, MS and MR classes. The classification results of the proposed CT based features are compared with existing approaches for the automated classification of HVDs. The proposed approach has obtained the highest overall accuracy as compared to existing methods using the same database. The approach can be considered for the automated detection of HVDs with the Internet of Medical Things (IOMT) applications.
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