Enhanced Identification of Valvular Heart Diseases through Selective Phonocardiogram Features Driven by Convolutional Neural Networks (SFD-CNN)
Main Article Content
Keywords
Valvular heart disease, PCG, classification, deep learning
Abstract
Valvular Heart Disease (VHD) is a significant cause of mortality worldwide. Although extensive research has been conducted to address this issue, practical implementation of existing VHD detection results in medicine still falls short of optimal performance. Recent investigations into machine learning for VHD detection have achieved commendable accuracy, sensitivity, and robustness. To address this limitation, our research proposes utilizing Selective Phonocardiogram Features Driven by Convolutional Neural Networks (SFD-CNN) to enhance VHD detection. Notably, SFD-CNN operates on phonocardiogram (PCG) signals, distinguishing itself from existing methods based on electrocardiogram (ECG) signals. We present two experimental scenarios to assess the performance of SFD-CNN: one under default parameter conditions and another with hyperparameter tuning. The experimental results demonstrate that SFD-CNN surpasses other existing models, achieving outstanding accuracy (96.80%), precision (93.25%), sensitivity (91.99%), specificity (98.00%), and F1-score (92.09%). The outstanding performance of SFD-CNN in VHD detection suggests that it holds great promise for practical use in various medical applications. Its potential lies in its ability to accurately identify and classify VHD, enabling early detection and timely intervention. SFD-CNN could significantly improve patient outcomes and reduce the burden on healthcare systems. With further development and refinement, SFD-CNN has the potential to revolutionize the field of VHD detection and become an indispensable tool for healthcare professionals.
References
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[19] J. Karhade, S. Dash, S. K. Ghosh, D. K. Dash, and R. K. Tripathy, "Time-Frequency-Domain Deep Learning Framework for the Automated Detection of Heart Valve Disorders Using PCG Signals," IEEE Trans Instrum Meas, vol. 71, 2022, doi: 10.1109/TIM.2022.3163156.
[20] T. Sinha Roy, J. K. Roy, and N. Mandal, "Conv-Random Forest-Based IoT: A Deep Learning Model Based on CNN and Random Forest for Classification and Analysis of Valvular Heart Diseases," IEEE Open Journal of Instrumentation and Measurement, vol. 2, pp. 1–17, Sep. 2023, doi: 10.1109/ojim.2023.3320765.
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[22] S. I. Flores-Alonso, B. Tovar-Corona, and R. Luna-García, "Deep Learning Algorithm for Heart Valve Diseases Assisted Diagnosis," Applied Sciences 2022, Vol. 12, Page 3780, vol. 12, no. 8, p. 3780, Apr. 2022, doi: 10.3390/APP12083780.
[23] A. M. Alqudah, H. Alquran, and I. A. Qasmieh, "Classification of heart sound short records using bispectrum analysis approach images and deep learning," Network Modeling Analysis in Health Informatics and Bioinformatics, vol. 9, no. 1, Dec. 2020, doi: 10.1007/s13721-020-00272-5.
[24] S. A. Singh, T. G. Meitei, and S. Majumder, "Short PCG classification based on deep learning," in Deep Learning Techniques for Biomedical and Health Informatics, Elsevier Inc., 2020, pp. 141–164. doi: 10.1016/B978-0-12-819061-6.00006-9.
[25] Ö. Arslan and M. Karhan, "Effect of Hilbert-Huang transform on classification of PCG signals using machine learning," Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 10, pp. 9915–9925, Nov. 2022, doi: 10.1016/J.JKSUCI.2021.12.019.
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[27] S. Mandala, Y. N. Fuadah, M. Arzaki, and F. E. Pambudi, Performance Analysis of Wavelet-Based Denoising Techniques for ECG Signal. 2017.
[28] J. Leo, C. Loong, K. S. Subari, M. K. Abdullah, N. Ahmad, and R. Besar, "Comparison of MFCC and Cepstral Coefficients as a Feature Set for PCG Biometric Systems."
[29] P. Singh, S. Waldekar, M. Sahidullah, G. Saha, and S. Goutam, "Analysis of constant-Q filterbank based representations for speech emotion recognition Analysis of constant-Q filterbank based representations for speech emotion recognition Analysis of constant-Q filterbank based representations for speech emotion recognition," Digit Signal Process, vol. 130, p. 103712, 2022, doi: 10.1016/j.dsp.2022.103712ï.
[30] J. K. Das, A. Ghosh, A. K. Pal, S. Dutta, and A. Chakrabarty, "Urban Sound Classification Using Convolutional Neural Network and Long Short Term Memory Based on Multiple Features," 4th International Conference on Intelligent Computing in Data Sciences, ICDS 2020, Oct. 2020, doi: 10.1109/ICDS50568.2020.9268723.
[31] H. Jeon, Y. Jung, S. Lee, and Y. Jung, "Area-efficient short-time fourier transform processor for time–frequency analysis of non-stationary signals," Applied Sciences (Switzerland), vol. 10, no. 20, pp. 1–10, Oct. 2020, doi: 10.3390/app10207208.
[32] S. Fitriani, S. Mandala, and M. A. Murti, "Review of semi-supervised method for Intrusion Detection System," in 2016 Asia Pacific Conference on Multimedia and Broadcasting (APMediaCast), 2016, pp. 36–41. doi: 10.1109/APMediaCast.2016.7878168.
[33] I. K. Nti, O. Nyarko-Boateng, and J. Aning, "Performance of Machine Learning Algorithms with Different K Values in K-fold CrossValidation," International Journal of Information Technology and Computer Science, vol. 13, no. 6, pp. 61–71, Dec. 2021, doi: 10.5815/ijitcs.2021.06.05.
[34] M. T. H. Chowdhury, K. N. Poudel, and Y. Hu, "Automatic Phonocardiography Analysis Using Discrete Wavelet Transform," in ACM International Conference Proceeding Series, Association for Computing Machinery, Aug. 2019. doi: 10.1145/3387168.3387172.
[35] M. M. Taye, "Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions," Computers, vol. 12, no. 5. MDPI, May 01, 2023. doi: 10.3390/computers12050091.
[2] M. Tung, G. Nah, J. Tang, G. Marcus, and F. N. Delling, "Valvular disease burden in the modern era of percutaneous and surgical interventions: The UK Biobank," Open Heart, vol. 9, no. 2, Sep. 2022, doi: 10.1136/openhrt-2022-002039.
[3] Y. Coulibaly, A. A. I. Al-Kilany, M. S. A. Latiff, G. Rouskas, S. Mandala, and M. A. Razzaque, "Secure burst control packet scheme for Optical Burst Switching networks," in 2015 IEEE International Broadband and Photonics Conference (IBP), IEEE, 2015, pp. 86–91. doi: 10.1109/IBP.2015.7230771.
[4] G. D. Clifford et al., "Classification of Normal/Abnormal Heart Sound Recordings: the PhysioNet/Computing in Cardiology Challenge 2016."
[5] M. Alkhodari and L. Fraiwan, "Convolutional and recurrent neural networks for the detection of valvular heart diseases in phonocardiogram recordings," Comput Methods Programs Biomed, vol. 200, p. 105940, Mar. 2021, doi: 10.1016/J.CMPB.2021.105940.
[6] Yaseen, G. Y. Son, and S. Kwon, “Classification of Heart Sound Signal Using Multiple Features,” Applied Sciences 2018, Vol. 8, Page 2344, vol. 8, no. 12, p. 2344, Nov. 2018, doi: 10.3390/APP8122344.
[7] M. Yaumil, I. #1, S. Mandala, and M. Pramudyo, "Study of Denoising Method to Detect Valvular Heart Disease Using Phonocardiogram (PCG)," Indonesia Journal on Computing (Indo-JC), vol. 7, no. 1, pp. 31–38, Apr. 2022, doi: 10.34818/INDOJC.2022.7.1.610.
[8] P. J. Khade, P. Mane, S. Mahore, and K. Bhole, "Machine Learning Approach for Prediction of Aortic and Mitral Regurgitation based on Phonocardiogram Signal," in 2021 12th International Conference on Computing Communication and Networking Technologies, ICCCNT 2021, Institute of Electrical and Electronics Engineers Inc., 2021. doi: 10.1109/ICCCNT51525.2021.9579971.
[9] M. Farhan, S. Mandala, and M. Pramudyo, "Detecting Heart Valve Disease Using Support Vector Machine Algorithm based on Phonocardiogram Signal," in 2021 International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA), 2021, pp. 128–132. doi: 10.1109/ICICyTA53712.2021.9689142.
[10] W. R. Putra, S. Mandala, and M. Pramudyo, "Study of Feature Extraction Methods to Detect Valvular Heart Disease (VHD) Using a Phonocardiogram," in 2021 International Conference on Intelligent Cybernetics Technology & Applications (ICICyTA), 2021, pp. 122–127. doi: 10.1109/ICICyTA53712.2021.9689119.
[11] V. Arora, R. Leekha, R. Singh, and I. Chana, "Heart sound classification using machine learning and phonocardiogram," https://doi.org/10.1142/S0217984919503214, vol. 33, no. 26, Sep. 2019, doi: 10.1142/S0217984919503214.
[12] S. K. Ghosh, R. K. Tripathy, R. N. Ponnalagu, and R. B. Pachori, "Automated Detection of Heart Valve Disorders from the PCG Signal Using Time-Frequency Magnitude and Phase Features," IEEE Sens Lett, vol. 3, no. 12, Dec. 2019, doi: 10.1109/LSENS.2019.2949170.
[13] T. Tuncer, S. Dogan, R. S. Tan, and U. R. Acharya, "Application of Petersen graph pattern technique for automated detection of heart valve diseases with PCG signals," Inf Sci (N Y), vol. 565, pp. 91–104, Jul. 2021, doi: 10.1016/j.ins.2021.01.088.
[14] S. Mandala, Y. N. Fuadah, M. Arzaki, and F. E. Pambudi, Performance Analysis of Wavelet-Based Denoising Techniques for ECG Signal. 2017.
[15] R. I. Aljohani, H. A. Hosni Mahmoud, A. Hafez, and M. Bayoumi, "A Novel Deep Learning CNN for Heart Valve Disease Classification Using Valve Sound Detection," Electronics 2023, Vol. 12, Page 846, vol. 12, no. 4, p. 846, Feb. 2023, doi: 10.3390/ELECTRONICS12040846.
[16] A. M. Alqudah, S. Qazan, and A. Alqudah, "Detection of Valvular Heart Diseases Using Fourier Transform and Simple CNN Model," Article in IAENG International Journal of Computer Science, Accessed: April 28, 2023. [Online]. Available: https://www.researchgate.net/publication/365993591
[17] J. Li, L. Ke, and Q. Du, "Classification of heart sounds based on thewavelet fractal and twin support vector machine," Entropy, vol. 21, no. 5, May 2019, doi: 10.3390/e21050472.
[18] N. Binta, I. Sabur, K. Nuhash, and T. Hasan, "Hilbert-Envelope Features for Cardiac Disease Classification from Noisy Phonocardiograms", doi: 10.1101/2020.11.17.20233064.
[19] J. Karhade, S. Dash, S. K. Ghosh, D. K. Dash, and R. K. Tripathy, "Time-Frequency-Domain Deep Learning Framework for the Automated Detection of Heart Valve Disorders Using PCG Signals," IEEE Trans Instrum Meas, vol. 71, 2022, doi: 10.1109/TIM.2022.3163156.
[20] T. Sinha Roy, J. K. Roy, and N. Mandal, "Conv-Random Forest-Based IoT: A Deep Learning Model Based on CNN and Random Forest for Classification and Analysis of Valvular Heart Diseases," IEEE Open Journal of Instrumentation and Measurement, vol. 2, pp. 1–17, Sep. 2023, doi: 10.1109/ojim.2023.3320765.
[21] T. S. Roy, J. K. Roy, and N. Mandal, "Classifier identification using deep learning and machine learning algorithms for the detection of valvular heart diseases," Biomedical Engineering Advances, vol. 3, p. 100035, Jun. 2022, doi: 10.1016/J.BEA.2022.100035.
[22] S. I. Flores-Alonso, B. Tovar-Corona, and R. Luna-García, "Deep Learning Algorithm for Heart Valve Diseases Assisted Diagnosis," Applied Sciences 2022, Vol. 12, Page 3780, vol. 12, no. 8, p. 3780, Apr. 2022, doi: 10.3390/APP12083780.
[23] A. M. Alqudah, H. Alquran, and I. A. Qasmieh, "Classification of heart sound short records using bispectrum analysis approach images and deep learning," Network Modeling Analysis in Health Informatics and Bioinformatics, vol. 9, no. 1, Dec. 2020, doi: 10.1007/s13721-020-00272-5.
[24] S. A. Singh, T. G. Meitei, and S. Majumder, "Short PCG classification based on deep learning," in Deep Learning Techniques for Biomedical and Health Informatics, Elsevier Inc., 2020, pp. 141–164. doi: 10.1016/B978-0-12-819061-6.00006-9.
[25] Ö. Arslan and M. Karhan, "Effect of Hilbert-Huang transform on classification of PCG signals using machine learning," Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 10, pp. 9915–9925, Nov. 2022, doi: 10.1016/J.JKSUCI.2021.12.019.
[26] Amelia F and Gunawan D, "DWT-MFCC Method for Speaker Recognition System with Noise," in 2019 7th International Conference on Smart Computing & Communications (ICSCC)., 2019. doi: 10.1109/ICSCC.2019.8843660.
[27] S. Mandala, Y. N. Fuadah, M. Arzaki, and F. E. Pambudi, Performance Analysis of Wavelet-Based Denoising Techniques for ECG Signal. 2017.
[28] J. Leo, C. Loong, K. S. Subari, M. K. Abdullah, N. Ahmad, and R. Besar, "Comparison of MFCC and Cepstral Coefficients as a Feature Set for PCG Biometric Systems."
[29] P. Singh, S. Waldekar, M. Sahidullah, G. Saha, and S. Goutam, "Analysis of constant-Q filterbank based representations for speech emotion recognition Analysis of constant-Q filterbank based representations for speech emotion recognition Analysis of constant-Q filterbank based representations for speech emotion recognition," Digit Signal Process, vol. 130, p. 103712, 2022, doi: 10.1016/j.dsp.2022.103712ï.
[30] J. K. Das, A. Ghosh, A. K. Pal, S. Dutta, and A. Chakrabarty, "Urban Sound Classification Using Convolutional Neural Network and Long Short Term Memory Based on Multiple Features," 4th International Conference on Intelligent Computing in Data Sciences, ICDS 2020, Oct. 2020, doi: 10.1109/ICDS50568.2020.9268723.
[31] H. Jeon, Y. Jung, S. Lee, and Y. Jung, "Area-efficient short-time fourier transform processor for time–frequency analysis of non-stationary signals," Applied Sciences (Switzerland), vol. 10, no. 20, pp. 1–10, Oct. 2020, doi: 10.3390/app10207208.
[32] S. Fitriani, S. Mandala, and M. A. Murti, "Review of semi-supervised method for Intrusion Detection System," in 2016 Asia Pacific Conference on Multimedia and Broadcasting (APMediaCast), 2016, pp. 36–41. doi: 10.1109/APMediaCast.2016.7878168.
[33] I. K. Nti, O. Nyarko-Boateng, and J. Aning, "Performance of Machine Learning Algorithms with Different K Values in K-fold CrossValidation," International Journal of Information Technology and Computer Science, vol. 13, no. 6, pp. 61–71, Dec. 2021, doi: 10.5815/ijitcs.2021.06.05.
[34] M. T. H. Chowdhury, K. N. Poudel, and Y. Hu, "Automatic Phonocardiography Analysis Using Discrete Wavelet Transform," in ACM International Conference Proceeding Series, Association for Computing Machinery, Aug. 2019. doi: 10.1145/3387168.3387172.
[35] M. M. Taye, "Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions," Computers, vol. 12, no. 5. MDPI, May 01, 2023. doi: 10.3390/computers12050091.
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Mar 29, 2024
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Muhammad Rafli Ramadhan, Mandala, S., Rafi Ullah, Wael M.S. Yafooz, & Muhammad Qomaruddin. (2024). Enhanced Identification of Valvular Heart Diseases through Selective Phonocardiogram Features Driven by Convolutional Neural Networks (SFD-CNN). Jurnal Nasional Teknik Elektro, 13(1), 20–35. https://doi.org/10.25077/jnte.v13n1.1184.2024
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