Sistem Deteksi Dini Penyakit Parkinson Melalui Speech Pattern Menggunakan Metode Recurrence Quantification Analysis
Kata Kunci:
Parkinson, pendeteksian dini, RQA, pola bicara, RaspBerry Pi 4 Model B, CNNAbstrak
Saat ini, total pengidap penyakit Parkinson di seluruh dunia sudah melebihi 10 juta orang. Penyakit Parkinson adalah gangguan neurodegenerative yang dapat mengganggu kemampuan kognitif dan motorik penderita. Penelitian ini bertujuan untuk mengembangkan suatu sistem yang dapat melakukan pendeteksian dini penyakit Parkinson melalui pola suara. Metode Recurrence Quantification Analysis (RQA) terpilih sebagai metode ekstraksi fitur dikarenakan kemampuannya untuk menganalisis dan mengidentifikasi karakteristik nonlinier dan kompleksitas dalam pola bicara. RQA memungkinkan pemeriksaan menyeluruh terhadap komponen sistem nonlinier seperti pola bicara pasien Parkinson dibandingkan dengan orang sehat. Metode tersebut nantinya diimplementasikan ke dalam Raspberry Pi 4 Model B yang akan didukung oleh komponen lainnya. Selain itu, pendekatan berbasis deep learning, khususnya Convolutional Neural Network (CNN) juga digunakan untuk menemukan pola-pola yang dapat dipelajari untuk melakukan klasifikasi. Dataset yang digunakan diambil dari internet. Dari hasil ekstraksi fitur yang dilakukan oleh RQA, diketahui bahwa terdapat pola yang berbeda dari pengidap penyakit Parkinson dan non-parkinson. Hasil dari penilitian menunjukkan bahwa sistem yang menggunakan RQA sebagai ekstraksi fitur dan CNN sebagai model neural network dapat menghasilkan tingkat ketepatan klasifikasi hingga 86%. Oleh karena itu, sistem ini memiliki potensi sebagai perangkat pembantu pendeteksian dini penyakit Parkinson.
Referensi
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Bayat, O., Aljawarneh, S., Carlak, H. F., International Association of Researchers, Institute of Electrical and Electronics Engineers, & Akdeniz Üniversitesi. (2017). Proceedings of 2017 International Conference on Engineering & Technology (ICET’2017) : Akdeniz University, Antalya, Turkey, 21-23 August, 2017.
Bloem, B. R., Okun, M. S., & Klein, C. (2021). Parkinson’s disease. In The Lancet (Vol. 397, Issue 10291, pp. 2284–2303). Elsevier B.V. https://doi.org/10.1016/S0140-6736(21)00218-X
Coco, M. I., Mønster, D., Leonardi, G., Dale, R., & Wallot, S. (n.d.). Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis with crqa.
Cohen, S. M., Kim, J., Roy, N., Asche, C., & Courey, M. (2012). Prevalence and causes of dysphonia in a large treatment-seeking population. Laryngoscope, 122(2), 343–348. https://doi.org/10.1002/lary.22426
Curtin, P., Curtin, A., Austin, C., Gennings, C., Tammimies, K., Bölte, S., & Arora, M. (2017). Recurrence quantification analysis to characterize cyclical components of environmental elemental exposures during fetal and postnatal development. PLoS ONE, 12(11). https://doi.org/10.1371/journal.pone.0187049
Dahmani, M., & Guerti, M. (2020). Recurrence quantification analysis of glottal signal as non linear tool for pathological voice assessment and classification. International Arab Journal of Information Technology, 17(6), 857–866. https://doi.org/10.34028/iajit/17/6/4
De, W. C., Costa, A., Assis, F. M., Aguiar Neto, B. G., Cunha Costa, S., & Dias Vieira, V. J. (n.d.). Pathological Voice Assessment By Recurrence Quantification Analysis.
Douglas, H. M., Furst-Holloway, S., Chaudoir, S. R., Richardson, M. J., & Kallen, R. W. (2022). Embodiment of concealable stigma disclosure through dynamics of movement and language. Humanities and Social Sciences Communications, 9(1). https://doi.org/10.1057/s41599-022-01226-0
Dutsinma, F. L. I., Pal, D., Funilkul, S., & Chan, J. H. (2022). A Systematic Review of Voice Assistant Usability: An ISO 9241–11 Approach. SN Computer Science, 3(4). https://doi.org/10.1007/s42979-022-01172-3
Gao, X., Yan, X., Gao, P., Gao, X., & Zhang, S. (2020). Automatic detection of epileptic seizure based on approximate entropy, recurrence quantification analysis and convolutional neural networks. Artificial Intelligence in Medicine, 102. https://doi.org/10.1016/j.artmed.2019.101711
Hegde, S., Shetty, S., Rai, S., & Dodderi, T. (2019). A Survey on Machine Learning Approaches for Automatic Detection of Voice Disorders. In Journal of Voice (Vol. 33, Issue 6, pp. 947.e11-947.e33). Mosby Inc. https://doi.org/10.1016/j.jvoice.2018.07.014
Islam, R., Abdel-Raheem, E., & Tarique, M. (2022). Voice pathology detection using convolutional neural networks with electroglottographic (EGG) and speech signals. Computer Methods and Programs in Biomedicine Update, 2. https://doi.org/10.1016/j.cmpbup.2022.100074
Lames, M., Hermann, S., Prüßner, R., & Meth, H. (2021). Football Match Dynamics Explored by Recurrence Analysis. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.747058
Li, Z., Yang, W., Peng, S., & Liu, F. (n.d.). A Survey of Convolutional Neural NetworksAnalysis, Applications, and Prospects.
Lopes, L. W., Vieira, V. J. D., Costa, S. L. do N. C., Correia, S. É. N., & Behlau, M. (2020). Effectiveness of Recurrence Quantification Measures in Discriminating Subjects With and Without Voice Disorders. Journal of Voice, 34(2), 208–220. https://doi.org/10.1016/j.jvoice.2018.09.004
Nainggolan, K. R., Purnamasari, F., & Pulungan, A. F. (2023). Prediksi Penyakit Parkinson Melalui Dataset Rekam Suara Dengan Menggunakan Algoritma Deep Neural Network. Jurnal Minfo Polgan, 12(1). https://doi.org/10.33395/jmp.v12i1.12985
Nanni, L., Maguolo, G., Brahnam, S., & Paci, M. (2021). An ensemble of convolutional neural networks for audio classification. Applied Sciences (Switzerland), 11(13). https://doi.org/10.3390/app11135796
Olson Ramig, L., Fox, C., & Sapir, S. (n.d.). Speech disorders in Parkinson’s disease and the effects of pharmacological, surgical and speech treatment with emphasis on Lee Silverman voice treatment (LSVT Ò ).
Ramig, L. O., Fox, C., & Sapir, S. (2008). Speech treatment for Parkinson’s disease. In Expert Review of Neurotherapeutics (Vol. 8, Issue 2, pp. 297–309). https://doi.org/10.1586/14737175.8.2.297
Supriana Suwardi, I., Latifah Erawati Rajab, T., & Puji Lestari, D. (2019). Kajian Penelitian Pemrosesan Bunyi dan Aplikasinya pada Teknologi Informasi (Study of Sound Processing and Application on Information Technology): Vol. VII (Issue 1).
Tadse, S., Jain, M., & Chandankhede, P. (2021). Parkinson’s detection using machine learning. Proceedings - 5th International Conference on Intelligent Computing and Control Systems, ICICCS 2021, 1081–1085. https://doi.org/10.1109/ICICCS51141.2021.9432340
Tolosa, E., Garrido, A., Scholz, S. W., & Poewe, W. (2021). Challenges in the diagnosis of Parkinson’s disease. In The Lancet Neurology (Vol. 20, Issue 5, pp. 385–397). Lancet Publishing Group. https://doi.org/10.1016/S1474-4422(21)00030-2
Wahyuningtyas, V. (2021). Implementasi Ekstraksi Fitur untuk Klasifikasi Suara Urban Menggunakan Deep Learning (Vol. 3, Issue 1).
Zhu, X. C., Zhao, D. H., Zhang, Y. H., Zhang, X. J., & Tao, Z. (2022). Multi-Scale Recurrence Quantification Measurements for Voice Disorder Detection. Applied Sciences (Switzerland), 12(18). https://doi.org/10.3390/app12189196
Alia, S., Hidayati, H. B., Hamdan, M., Nugraha, P., Fahmi, A., Turchan, A., & Haryono, Y. (2021). Penyakit Parkinson: Tinjauan Tentang Salah Satu Penyakit Neurodegeneratif yang Paling Umum (Vol. 1, Issue 2).
Bayat, O., Aljawarneh, S., Carlak, H. F., International Association of Researchers, Institute of Electrical and Electronics Engineers, & Akdeniz Üniversitesi. (2017). Proceedings of 2017 International Conference on Engineering & Technology (ICET’2017) : Akdeniz University, Antalya, Turkey, 21-23 August, 2017.
Bloem, B. R., Okun, M. S., & Klein, C. (2021). Parkinson’s disease. In The Lancet (Vol. 397, Issue 10291, pp. 2284–2303). Elsevier B.V. https://doi.org/10.1016/S0140-6736(21)00218-X
Coco, M. I., Mønster, D., Leonardi, G., Dale, R., & Wallot, S. (n.d.). Unidimensional and Multidimensional Methods for Recurrence Quantification Analysis with crqa.
Cohen, S. M., Kim, J., Roy, N., Asche, C., & Courey, M. (2012). Prevalence and causes of dysphonia in a large treatment-seeking population. Laryngoscope, 122(2), 343–348. https://doi.org/10.1002/lary.22426
Curtin, P., Curtin, A., Austin, C., Gennings, C., Tammimies, K., Bölte, S., & Arora, M. (2017). Recurrence quantification analysis to characterize cyclical components of environmental elemental exposures during fetal and postnatal development. PLoS ONE, 12(11). https://doi.org/10.1371/journal.pone.0187049
Dahmani, M., & Guerti, M. (2020). Recurrence quantification analysis of glottal signal as non linear tool for pathological voice assessment and classification. International Arab Journal of Information Technology, 17(6), 857–866. https://doi.org/10.34028/iajit/17/6/4
De, W. C., Costa, A., Assis, F. M., Aguiar Neto, B. G., Cunha Costa, S., & Dias Vieira, V. J. (n.d.). Pathological Voice Assessment By Recurrence Quantification Analysis.
Douglas, H. M., Furst-Holloway, S., Chaudoir, S. R., Richardson, M. J., & Kallen, R. W. (2022). Embodiment of concealable stigma disclosure through dynamics of movement and language. Humanities and Social Sciences Communications, 9(1). https://doi.org/10.1057/s41599-022-01226-0
Dutsinma, F. L. I., Pal, D., Funilkul, S., & Chan, J. H. (2022). A Systematic Review of Voice Assistant Usability: An ISO 9241–11 Approach. SN Computer Science, 3(4). https://doi.org/10.1007/s42979-022-01172-3
Gao, X., Yan, X., Gao, P., Gao, X., & Zhang, S. (2020). Automatic detection of epileptic seizure based on approximate entropy, recurrence quantification analysis and convolutional neural networks. Artificial Intelligence in Medicine, 102. https://doi.org/10.1016/j.artmed.2019.101711
Hegde, S., Shetty, S., Rai, S., & Dodderi, T. (2019). A Survey on Machine Learning Approaches for Automatic Detection of Voice Disorders. In Journal of Voice (Vol. 33, Issue 6, pp. 947.e11-947.e33). Mosby Inc. https://doi.org/10.1016/j.jvoice.2018.07.014
Islam, R., Abdel-Raheem, E., & Tarique, M. (2022). Voice pathology detection using convolutional neural networks with electroglottographic (EGG) and speech signals. Computer Methods and Programs in Biomedicine Update, 2. https://doi.org/10.1016/j.cmpbup.2022.100074
Lames, M., Hermann, S., Prüßner, R., & Meth, H. (2021). Football Match Dynamics Explored by Recurrence Analysis. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.747058
Li, Z., Yang, W., Peng, S., & Liu, F. (n.d.). A Survey of Convolutional Neural NetworksAnalysis, Applications, and Prospects.
Lopes, L. W., Vieira, V. J. D., Costa, S. L. do N. C., Correia, S. É. N., & Behlau, M. (2020). Effectiveness of Recurrence Quantification Measures in Discriminating Subjects With and Without Voice Disorders. Journal of Voice, 34(2), 208–220. https://doi.org/10.1016/j.jvoice.2018.09.004
Nainggolan, K. R., Purnamasari, F., & Pulungan, A. F. (2023). Prediksi Penyakit Parkinson Melalui Dataset Rekam Suara Dengan Menggunakan Algoritma Deep Neural Network. Jurnal Minfo Polgan, 12(1). https://doi.org/10.33395/jmp.v12i1.12985
Nanni, L., Maguolo, G., Brahnam, S., & Paci, M. (2021). An ensemble of convolutional neural networks for audio classification. Applied Sciences (Switzerland), 11(13). https://doi.org/10.3390/app11135796
Olson Ramig, L., Fox, C., & Sapir, S. (n.d.). Speech disorders in Parkinson’s disease and the effects of pharmacological, surgical and speech treatment with emphasis on Lee Silverman voice treatment (LSVT Ò ).
Ramig, L. O., Fox, C., & Sapir, S. (2008). Speech treatment for Parkinson’s disease. In Expert Review of Neurotherapeutics (Vol. 8, Issue 2, pp. 297–309). https://doi.org/10.1586/14737175.8.2.297
Supriana Suwardi, I., Latifah Erawati Rajab, T., & Puji Lestari, D. (2019). Kajian Penelitian Pemrosesan Bunyi dan Aplikasinya pada Teknologi Informasi (Study of Sound Processing and Application on Information Technology): Vol. VII (Issue 1).
Tadse, S., Jain, M., & Chandankhede, P. (2021). Parkinson’s detection using machine learning. Proceedings - 5th International Conference on Intelligent Computing and Control Systems, ICICCS 2021, 1081–1085. https://doi.org/10.1109/ICICCS51141.2021.9432340
Tolosa, E., Garrido, A., Scholz, S. W., & Poewe, W. (2021). Challenges in the diagnosis of Parkinson’s disease. In The Lancet Neurology (Vol. 20, Issue 5, pp. 385–397). Lancet Publishing Group. https://doi.org/10.1016/S1474-4422(21)00030-2
Wahyuningtyas, V. (2021). Implementasi Ekstraksi Fitur untuk Klasifikasi Suara Urban Menggunakan Deep Learning (Vol. 3, Issue 1).
Zhu, X. C., Zhao, D. H., Zhang, Y. H., Zhang, X. J., & Tao, Z. (2022). Multi-Scale Recurrence Quantification Measurements for Voice Disorder Detection. Applied Sciences (Switzerland), 12(18). https://doi.org/10.3390/app12189196
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