Klasifikasi Suara Paru-Paru Berdasarkan Ciri MFCC

https://doi.org/10.22146/ijeis.70813

Dody Rafiqo(1*), Yohanes Suyanto(2), Catur Atmaji(3)

(1) Prodi Elektronika dan Instrumentasi FMIPA, UGM Yogyakarta
(2) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
(3) Departemen Ilmu Komputer dan Elektronika, FMIPA UGM, Yogyakarta
(*) Corresponding Author

Abstract


The lungs are an important organ in the human respiratory system, which functions to exchange carbon dioxide from the blood with oxygen in the air. Detection of respiratory disorders and lung disorders can be done in various ways; view medical records, physical examination, detection by x-ray and also auscultation of breathing. Digital signal processing can be used as a method to detect lung disorders based on the sound produced. In this study, lung sounds were classified into normal, crackle, wheeze, and crackle-wheeze classes using the Mel Frequency Cepstral Coefficient (MFCC) and Convolutional Neural Network (CNN) methods.

Observations were made by varying the MFCC feature extraction using MFCC 8 and 13 coefficients, the number of frames are 50 and 60, and the width of the frames used was 0,1, 0,15 and 0,2 seconds. The result of feature extraction is then applied to the CNN classification system, and the confusion matrix is used to get the accuracy and precision values. The highest accuracy and precision values were obtained at 71,85% and 65,70% on the MFCC 13 coefficient with an average of 71,18%. Based on these results, the system that has been created can classify normal lung sounds, crackle, wheeze and crackle-wheeze quite well.


Keywords


lung sounds; crackle; wheeze; MFCC’s frame; CNN

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References

[1] F. Syafria, A. Buono, and B. P. Silalahi, “Pengenalan Suara Paru-Paru dengan MFCC sebagai Ekstraksi Ciri dan Backpropagation sebagai Classifier,” J. Ilmu Komput. dan Agri-Informatika, vol. 3, no. 1, p. 27, 2017.

[2] I. W. Hasanain, A. Rizal, and Jondri, “Klasifikasi Suara Paru-Paru Menggunakan Convolutional Neural Network (CNN),” e-Proceeding Eng., vol. 8, no. 2, pp. 3218–3223, 2021.

[3] G. Chambres, P. Hanna, and M. Desainte-Catherine, “Automatic detection of patient with respiratory diseases using lung sound analysis,” Proc. - Int. Work. Content-Based Multimed. Index., 2018.

[4] G. Serbes, S. Ulukaya, and Y. P. Kahya, “An automated lung sound preprocessing and classification system based onspectral analysis methods,” IFMBE Proceedings, vol. 66. pp. 45–49, 2018.

[5] M. A. Romli and A. Solichin, “Pemrosesan Sinyal Digital Untuk Mengidentifikasi Akord Dasar Penyanyi Dengan Metode Mel Frequency Cepstral Coeficients (MFCC) Dan Jaringan Syaraf Tiruan Backpropagation. Digital Signal,” Semin. Nas. Multidisiplin Ilmu, 2017.

[6] I. B. L. M. Suta, R. S. Hartati, and Y. Divayana, “Diagnosa Tumor Otak Berdasarkan Citra MRI (Magnetic Resonance Imaging).,” JTE 18, 149–154, 2019.

[7] B. M. Rocha et al., “A respiratory sound database for the development of automated classification,” IFMBE Proc., vol. 66, pp. 33–37, 2018.

[8] P. S. Faustino, “Crackle and wheeze detection in lung sound signals using convolutional neural networks,” 2019. [online]. Available: https://repositorio-aberto.up.pt/bitstream/10216/125107/2/372897. [Accessed: 20-Okt-2020]

[9] J. K. A. Christya, C. Atmaji, and A. E. Putra, “Deteksi Kesalahan Pengucapan Huruf Jawa Carakan dengan Jaringan Syaraf Tiruan Perambatan Balik,” IJEIS, pp. 1–12, 2021.

[10] S. R. Dewi, “Deep Learning Object Detection pada Video menggunakan Tensorflow dan Convolutional Neural Network 95,” Skripsi, FMIPA, Universitas Islam Indonesia, Yogyakarta, 2018.

[11] R. A. Pangestu, B. Rahmat, and F. T. Anggraeny, “Implementasi Algoritma CNN untuk Klasifikasi Citra Lahan dan Perhitungan Luas,” Inform. dan Sist. Inf., vol. 1, no. 1, pp. 166–174, 2020.

[12] F. Prayogi, “Identifikasi Lahan Pertanian Menggunakan Convolutional Neural Network (CNN) Pada Citra Google Earth 188,” 2020. [online]. Available: https://github.com/SoedirmanMachineLearning/Identification_paddy_field. [Accessed: 25-Oct-2020]

[13] A. Wibowo, “10 Fold-Cross Validation,” 2017. [online]. available: https://mti.binus.ac.id/2017/11/24/10-fold-cross-validation/. [Accessed: 23-Sept-2021]

[14] S. Visa, B. Ramsay, A. Ralescu, and E. van der Knaap, “Confusion Matrix-based Feature Selection,” Conference Paper, vol. 710, pp.1-8.2011.

[15] A. Rizal, L. Anggraeni, and V. Suryani, “Pengenalan Suara Paru-Paru Normal Menggunakan LPC dan Jaringan Syaraf Tiruan Back-Propagation,” Preceeding Int. Semin. Electr. Power, Electron. Commun. Control. Informatics ( EECCIS 2006 ), pp. 6–10, 2006.



DOI: https://doi.org/10.22146/ijeis.70813

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