Early Detection of Mitral Valve Prolapse Disease Using Phonocardiogram Signal Analysis and Intelligent Classification System
Abstract views: 23
/ 
PDF downloads: 14
Keywords:
Mitral Valve Prolapses, Phonocardiogram, Features Extraction, Machine LearningAbstract
Mitral valve prolapse (MVP) is a common cardiovascular disorder that requires early
identification for proper management and treatment. In this study, our goal was to develop an effective
method for the early detection of MVP using phonocardiogram (PCG) signals through feature extraction
and classification techniques. The dataset comprised 400 PCG signals, including 200 normal PCG signals
and 200 MVP PCG signals.
To preprocess the data, a digital filtering technique employing 39 filters was applied to each signal.
Subsequently, a feature extraction algorithm was employed, enabling the extraction of 24 relevant features
from each PCG signal. These features encompassed various temporal and spectral characteristics of the
signals, capturing important information related to the presence of MVP.
For classification, we employed four popular machine learning algorithms: Decision Tree, Support Vector
Machine (SVM), K-Nearest Neighbors (KNN), and Ensemble. The performance of each classifier was
evaluated using a comprehensive set of evaluation metrics. The Decision Tree classifier achieved an
impressive accuracy of 100%, while SVM achieved 97.5% accuracy, KNN achieved 95% accuracy,
Ensemble achieved 98.8% accuracy, and Neural Network achieved 96.3% accuracy in distinguishing
between normal and MVP PCG signals.
The results demonstrate the potential of PCG signal analysis in the early detection of MVP. The high
classification accuracies achieved by the employed classifiers highlight the effectiveness of the proposed
approach. The findings of this study have significant implications for improving the diagnosis and timely
management of MVP, potentially leading to better patient outcomes and reduced healthcare costs.
Downloads
References
Zhang, H., Zhang, P., Lin, F., Chao, L., Wang, Z., Ma, F., & Li, Q. (2024). Co-learning-assisted progressive dense fusion network for cardiovascular disease detection using ECG and PCG signals. Expert Systems with Applications, 238, 122144.
Lin, C. H., Lai, H. Y., Huang, P. T., Chen, P. Y., & Li, C. M. (2023). Vowel classification with combining pitch detection and one‐dimensional convolutional neural network based classifier for gender identification. IET Signal Processing, 17(5), e12216.
Radha, K., Bansal, M., & Sharma, R. (2024). Raw Waveform-Based Custom Scalogram CRNN in Cardiac Abnormality Diagnosis. IEEE Access.
Rajeshwari, B. S., Patra, M., Sinha, A., Sengupta, A., & Ghosh, N. (2023). Detection of Phonocardiogram Event Patterns in Mitral Valve Prolapse: An Automated Clinically Relevant Explainable Diagnostic Framework. IEEE Transactions on Instrumentation and Measurement, 72, 1-9.
Rajeshwari, B. S., Sinha, A., Sengupta, A., Patra, M., Sahoo, K. P., & Ghosh, N. (2022, July). Synchrosqueezed transform based click event segmentation in phonocardiogram of mitral valve prolapse. In 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 1997-2000). IEEE.
Gupta, R., Sharma, S. D., & Kumar, Y. (2023). Heart Disease Prediction using Machine learning algorithms.
Shastri, R. K., Shastri, A. R., Nitnaware, P. P., & Padulkar, D. M. (2024). Hybrid Sneaky algorithm-based deep neural networks for Heart sound classification using phonocardiogram. Network: Computation in Neural Systems, 35(1), 1-26.
Zheng, Y., Guo, X., Wang, Y., Qin, J., & Lv, F. (2022). A multi-scale and multi-domain heart sound feature-based machine learning model for ACC/AHA heart failure stage classification. Physiological Measurement, 43(6), 065002.
Singh, G., Verma, A., Gupta, L., Mehta, A., & Arora, V. (2023). An automated diagnosis model for classifying cardiac abnormality utilizing deep neural networks. Multimedia Tools and Applications, 1-37.
Li, P., Hu, Y., & Liu, Z. P. (2021). Prediction of cardiovascular diseases by integrating multi-modal features with machine learning methods. Biomedical Signal Processing and Control, 66, 102474.
Jamil, S., & Roy, A. M. (2022). Robust pcg-based vhd detection model using d-cnns, nature-inspired algorithms, and vision transformer. Nature-Inspired Algorithms, and Vision Transformer (October 4, 2022).
Jamil, S., & Roy, A. M. (2023). An efficient and robust phonocardiography (pcg)-based valvular heart diseases (vhd) detection framework using vision transformer (vit). Computers in Biology and Medicine, 158, 106734.
Talal, M., Aziz, S., Khan, M. U., Ghadi, Y., Naqvi, S. Z. H., & Faraz, M. (2023). Machine learning‐based classification of multiple heart disorders from PCG signals. Expert Systems, 40(10), e13411.
Nabih-Ali, M., El-Dahshan, E. S. A., & Yahia, A. S. (2017). A review of intelligent systems for heart sound signal analysis. Journal of medical engineering & technology, 41(7), 553-563.
Gavrovska, A., Zajić, G., Reljin, I., & Reljin, B. (2013). Classification of prolapsed mitral valve versus healthy heart from phonocardiograms by multifractal analysis. Computational and mathematical methods in medicine, 2013.
