Multiclass Classification of Myocardial Infarction Based on Phonocardiogram Signals Using Ensemble Learning
Main Article Content
Keywords
multiclass classification, myocardial infarction, Phonocardiogram (PCG), ensemble learning.
Abstract
Myocardial infarction (MI) is a serious cardiovascular disease with a high mortality rate worldwide. Early detection and consistent treatment can significantly reduce mortality from cardiovascular diseases. However, there is a need for efficient models that can enable the early detection of heart disease without relying on trained clinical experts. MI studies using phonocardiogram (PCG) signals and implementing ensemble learning models are still relatively scarce, often resulting in poor accuracy and low detection rates. This study aims to implement an ensemble learning model for the classification of MI using PCG signals into different classes. In this stage of research, several classification algorithms, including Random Forest and Logistic Regression, serve as basic models for ensemble learning, utilizing features extracted from audio signals. Evaluation of the model's performance reveals that the stacking model achieves an accuracy of 96%. These results demonstrate that our system can appropriately and accurately classify MI within PCG data. We believe that the findings of this study will enhance the diagnosis and treatment of heart attacks, making them more effective and accurate.
References
[2] J.-S. Artikel, A. Prevalensi, D. F. Risiko, F. R. W. Suling, M. I. Patricia, and T. E. Suling, “Prevalensi dan Faktor Risiko Sindrom Koroner Akut di Rumah Sakit Umum Universitas Kristen Indonesia,” Majalah Kedokteran UKI, vol. 34, no. 3, pp. 110–114, Oct. 2018, doi: 10.33541/MKVOL34ISS2PP60.
[3] S. I. Ketut, W. P. Kiki, Y. Anak, A. Gede, and W. Pratama, “INFARK MIOKARD AKUT DENGAN ELEVASI SEGMEN ST (IMA-EST) ANTERIOR EKSTENSIF: LAPORAN KASUS,” Ganesha Medicina, vol. 2, no. 1, pp. 22–32, Jun. 2022, doi: 10.23887/GM.V2I1.47058.
[4] T. Nguyen et al., “ENSEMBLE LEARNING OF MYOCARDIAL DISPLACEMENTS FOR MYOCARDIAL INFARCTION DETECTION IN ECHOCARDIOGRAPHY,” 2023. [Online]. Available: https://github.com/vinuni-vishc/mi-detection-echo.
[5] “View of Electrocardiogram Abnormal Classification Based on Abnormality Signal Feature.” http://jnte.ft.unand.ac.id/index.php/jnte/article/view/829/433 (accessed Jun. 27, 2023).
[6] P. Li, Y. Hu, and Z. P. Liu, “Prediction of cardiovascular diseases by integrating multi-modal features with machine learning methods,” Biomed Signal Process Control, vol. 66, p. 102474, Apr. 2021, doi: 10.1016/J.BSPC.2021.102474.
[7] “Sci-Hub | PCG Classification Using Multidomain Features and SVM Classifier. BioMed Research International, 2018, 1–14 | 10.1155/2018/4205027.” https://sci-hub.se/https://doi.org/10.1155/2018/4205027 (accessed Jun. 16, 2023).
[8] “Sci-Hub | PCG Classification Using Multidomain Features and SVM Classifier. BioMed Research International, 2018, 1–14 | 10.1155/2018/4205027.” https://sci-hub.se/https://doi.org/10.1155/2018/4205027 (accessed Jun. 15, 2023).
[9] M. Baydoun, L. Safatly, H. Ghaziri, and A. El Hajj, “Analysis of heart sound anomalies using ensemble learning,” Biomed Signal Process Control, vol. 62, p. 102019, Sep. 2020, doi: 10.1016/J.BSPC.2020.102019.
[10] T. Nguyen et al., “Ensemble Learning of Myocardial Displacements for Myocardial Infarction Detection in Echocardiography,” Mar. 2023, Accessed: Jun. 22, 2023. [Online]. Available: https://arxiv.org/abs/2303.06744v1
[11] “View of Optimal Parameter Selection for DWT based PCG Denoising.” https://www.turcomat.org/index.php/turkbilmat/article/view/5441/4553 (accessed Jun. 16, 2023).
[12] S. A. A. Yusuf and R. Hidayat, “MFCC feature extraction and KNN classification in ECG signals,” 2019 6th International Conference on Information Technology, Computer and Electrical Engineering, ICITACEE 2019, Sep. 2019, doi: 10.1109/ICITACEE.2019.8904285.
[13] R. Hidayat, A. Bejo, S. Sumaryono, and A. Winursito, “Denoising speech for MFCC feature extraction using wavelet transformation in speech recognition system,” Proceedings of 2018 10th International Conference on Information Technology and Electrical Engineering: Smart Technology for Better Society, ICITEE 2018, pp. 280–284, Nov. 2018, doi: 10.1109/ICITEED.2018.8534807.
[14] Y. Arpitha, G. L. Madhumathi, and N. Balaji, “Spectrogram analysis of ECG signal and classification efficiency using MFCC feature extraction technique,” J Ambient Intell Humaniz Comput, vol. 13, no. 2, pp. 757–767, Feb. 2022, doi: 10.1007/S12652-021-02926-2/TABLES/3.
[15] R. Touahria, A. Hacine-Gharbi, and P. Ravier, “Discrete wavelet based features for PCG signal classification using hidden markov models,” ICPRAM 2021 - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods, pp. 334–340, 2021, doi: 10.5220/0010343003340340.
[16] S. Tiwari, A. Jain, A. K. Sharma, and K. Mohamad Almustafa, “Phonocardiogram signal based multi-class cardiac diagnostic decision support system,” IEEE Access, vol. 9, pp. 110710–110722, 2021, doi: 10.1109/ACCESS.2021.3103316.
[17] Z. Tariq, S. Khushal Shah, and Y. Lee, “Automatic Multimodal Heart Disease Classification using Phonocardiogram Signal,” Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020, pp. 3514–3521, Dec. 2020, doi: 10.1109/BIGDATA50022.2020.9378232.
[18] X. Dong, Z. Yu, W. Cao, Y. Shi, and Q. Ma, “A survey on ensemble learning,” Front Comput Sci, vol. 14, no. 2, pp. 241–258, Apr. 2020, doi: 10.1007/S11704-019-8208-Z/METRICS.
[19] M. Farhan, S. Mandala, and M. Pramudyo, “Detecting Heart Valve Disease Using Support Vector Machine Algorithm based on Phonocardiogram Signal,” 2021 International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2021, pp. 128–132, 2021, doi: 10.1109/ICICYTA53712.2021.9689142.
[20] “View of ANN-Based Electricity Theft Classification Technique for Limited Data Distribution Systems.” http://jnte.ft.unand.ac.id/index.php/jnte/article/view/1072/474 (accessed Jun. 20, 2023).
[21] S. Kamepalli, B. S. Rao, and K. Venkata Krishna Kishore, “Multi-Class Classification and Prediction of Heart Sounds Using Stacked LSTM to Detect Heart Sound Abnormalities,” in 2022 3rd International Conference for Emerging Technology (INCET), IEEE, May 2022, pp. 1–6. doi: 10.1109/INCET54531.2022.9825189.
[22] D. Shah, S. Patel, · Santosh, and K. Bharti, “Heart Disease Prediction using Machine Learning Techniques,” SN Computer Science 2020 1:6, vol. 1, no. 6, pp. 1–6, Oct. 2020, doi: 10.1007/S42979-020-00365-Y.
Article Sidebar
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
The authors declare that:
1. This paper has not been published in the same form elsewhere.
2. It will not be submitted anywhere else for publication prior to acceptance/rejection by this Journal.
3. A copyright permission is obtained for materials published elsewhere and which require this permission for reproduction.