Sistem Deteksi Dini Penyakit Parkinson Melalui Voice Pattern Menggunakan Fitur Jitter dan Shimmer
Kata Kunci:
Parkinson, Jitter, shimmer, pola bicara, Raspberry Pi 4 Model B, CNNAbstrak
Penyakit Parkinson, yang merupakan gangguan sistem syaraf yang progresif dan ditandai oleh kerusakan pada sel-saraf penghasil dopamin, dibutuhkan metode deteksi dini yang efektif agar pasien dapat segera mendapatkan penanganan yang lebih cepat dan tepat. Penelitian ini bertujuan untuk mengembangkan sistem deteksi dini penyakit Parkinson melalui analisis pola suara, dengan fokus pada penggunaan fitur Jitter dan Shimmer. Jitter mengukur variasi waktu antara siklus getaran suara, menunjukkan ketidakstabilan dalam produksi suara, sedangkan Shimmer mengukur variasi amplitude pada gelombang suara mengindikasikan perubahan dalam kualitas suara. Implementasi dilakukan pada Raspberry Pi 4 Model B, mengintegrasikan teknologi deep learning dengan arsitektur Convolutional Neural Network (CNN) untuk mengklasifikasikan rekaman suara menjadi kategori Parkinson dan non-Parkinson. Sistem ini dioperasikan dengan LCD dan GUI untuk memudahkan interaksi pengguna dan memastikan bahwa hasil analisis dapat ditampilkan secara efisien dan intuitif. Penelitian ini menggunakan Dataset dari IEEE DataPort dengan durasi rekaman suara yang telah disesuaikan menjadi 4 detik untuk memastikan keakuratan pengolahan data. Penerapan fitur Jitter dan Shimmer pada analisis variasi dalam frekuensi suara dan kestabilan amplitudo berhasil menunjukkan bahwa sistem dapat mendeteksi penyakit Parkinson dengan tingkat ketepatan prediksi hingga 89%. Kinerja alat dan sistem yang baik menunjukkan adanya kesempatan besar untuk pengembangan lebih lanjut.
Referensi
Albawi, S., Mohammed, T.A. and Al-Zawi, S., 2017. Understanding of a convolutional neural network. Proceedings of 2017 International Conference on Engineering and Technology, ICET 2017, 2018-January, pp.1–6. https://doi.org/10.1109/ICEngTechnol.2017.8308186.
Azadi, H., Akbarzadeh, M.R.T., Shoeibi, A. and Kobravi, H.R., 2021. Evaluating the Effect of Parkinson’s Disease on Jitter and Shimmer Speech Features. Advanced Biomedical Research, 10(1), p.54. https://doi.org/10.4103/abr.abr_254_21.
Daniel, N., Abdul, M., Gazda, M. and Drot, P., 2022. Convolutional neural network ensemble for Parkinson ’ s disease detection from voice recordings . 141 (November 2021). https://doi.org/10.1016/j.compbiomed.2021.105021.
de Oliveira, A.A., Dajer, M.E., Fernandes, P.O. and Teixeira, J.P., 2020. Clustering of Voice Pathologies based on Sustained Voice Parameters. BIOSIGNALS 2020 - 13th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020, (Biostec), pp.280–287. https://doi.org/10.5220/0009146202800287.
Gillivan-Murphy, P., Miller, N., Carding, P., 2019. Voice Tremor in Parkinson’s Disease: An Acoustic Study. J. Voice 33, 526–535. https://doi.org/10.1016/j.jvoice.2017.12.010
Islam, M.R., Matin, A., Nahiduzzaman, M., Siddiquee, M.S., Hasnain, F.M.S., Shovan, S.M., Hasan, T., 2021. A Novel Deep Convolutional Neural Network Model for Detection of Parkinson Disease by Analysing the Spiral Drawing 155–165. https://doi.org/10.1007/978-981-16-0586-4_13
Li, K., Lu, X., Akagi, M. and Unoki, M., 2023. Contributions of Jitter and Shimmer in the Voice for Fake Audio Detection. IEEE Access, 11(August), pp.84689–84698. https://doi.org/10.1109/ACCESS.2023.3301616.
Ma, A., Lau, K.K., Thyagarajan, D., 2020. Voice changes in Parkinson’s disease: What are they telling us? J. Clin. Neurosci. 72, 1–7. https://doi.org/10.1016/j.jocn.2019.12.029
Narendra, N.P., Schuller, B. and Alku, P., 2021. The Detection of Parkinson’s Disease from Speech Using Voice Source Information. IEEE/ACM Transactions on Audio Speech and Language Processing, 29, pp.1925–1936. https://doi.org/10.1109/TASLP.2021.3078364.
Ranny, R., Suwardi, I.S., Rajab, T.L.E. and Lestari, D.P., 2019. Kajian Penelitian Pemrosesan Bunyi dan Aplikasinya pada Teknologi Informasi. JUITA : Jurnal Informatika, 7(1), p.1. https://doi.org/10.30595/juita.v7i1.3491.
Teixeira, J.P., Gonçalves, A., 2016. Algorithm for Jitter and Shimmer Measurement in Pathologic Voices. Procedia Comput. Sci. 100, 271–279. https://doi.org/10.1016/j.procs.2016.09.155
Teixeira, J.P., Gonçalves, A., 2014. Accuracy of Jitter and Shimmer Measurements. Procedia Technol. 16, 1190–1199. https://doi.org/10.1016/j.protcy.2014.10.134
Upadhya, S.S., Cheeran, A.N., Nirmal, J.H., 2017. Statistical comparison of Jitter and Shimmer voice features for healthy and Parkinson affected persons. Proc. 2017 2nd IEEE Int. Conf. Electr. Comput. Commun. Technol. ICECCT 2017 1–6. https://doi.org/10.1109/ICECCT.2017.8117853
Wahyuningtyas, V., 2021. Implementasi Ekstraksi Fitur untuk Klasifikasi Suara Urban Menggunakan Deep Learning. Sains, Aplikasi, Komputasi dan Teknologi Informasi, 3(1), pp.10–17.
Albawi, S., Mohammed, T.A. and Al-Zawi, S., 2017. Understanding of a convolutional neural network. Proceedings of 2017 International Conference on Engineering and Technology, ICET 2017, 2018-January, pp.1–6. https://doi.org/10.1109/ICEngTechnol.2017.8308186.
Azadi, H., Akbarzadeh, M.R.T., Shoeibi, A. and Kobravi, H.R., 2021. Evaluating the Effect of Parkinson’s Disease on Jitter and Shimmer Speech Features. Advanced Biomedical Research, 10(1), p.54. https://doi.org/10.4103/abr.abr_254_21.
Daniel, N., Abdul, M., Gazda, M. and Drot, P., 2022. Convolutional neural network ensemble for Parkinson ’ s disease detection from voice recordings . 141 (November 2021). https://doi.org/10.1016/j.compbiomed.2021.105021.
de Oliveira, A.A., Dajer, M.E., Fernandes, P.O. and Teixeira, J.P., 2020. Clustering of Voice Pathologies based on Sustained Voice Parameters. BIOSIGNALS 2020 - 13th International Conference on Bio-Inspired Systems and Signal Processing, Proceedings; Part of 13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020, (Biostec), pp.280–287. https://doi.org/10.5220/0009146202800287.
Gillivan-Murphy, P., Miller, N., Carding, P., 2019. Voice Tremor in Parkinson’s Disease: An Acoustic Study. J. Voice 33, 526–535. https://doi.org/10.1016/j.jvoice.2017.12.010
Islam, M.R., Matin, A., Nahiduzzaman, M., Siddiquee, M.S., Hasnain, F.M.S., Shovan, S.M., Hasan, T., 2021. A Novel Deep Convolutional Neural Network Model for Detection of Parkinson Disease by Analysing the Spiral Drawing 155–165. https://doi.org/10.1007/978-981-16-0586-4_13
Li, K., Lu, X., Akagi, M. and Unoki, M., 2023. Contributions of Jitter and Shimmer in the Voice for Fake Audio Detection. IEEE Access, 11(August), pp.84689–84698. https://doi.org/10.1109/ACCESS.2023.3301616.
Ma, A., Lau, K.K., Thyagarajan, D., 2020. Voice changes in Parkinson’s disease: What are they telling us? J. Clin. Neurosci. 72, 1–7. https://doi.org/10.1016/j.jocn.2019.12.029
Narendra, N.P., Schuller, B. and Alku, P., 2021. The Detection of Parkinson’s Disease from Speech Using Voice Source Information. IEEE/ACM Transactions on Audio Speech and Language Processing, 29, pp.1925–1936. https://doi.org/10.1109/TASLP.2021.3078364.
Ranny, R., Suwardi, I.S., Rajab, T.L.E. and Lestari, D.P., 2019. Kajian Penelitian Pemrosesan Bunyi dan Aplikasinya pada Teknologi Informasi. JUITA : Jurnal Informatika, 7(1), p.1. https://doi.org/10.30595/juita.v7i1.3491.
Teixeira, J.P., Gonçalves, A., 2016. Algorithm for Jitter and Shimmer Measurement in Pathologic Voices. Procedia Comput. Sci. 100, 271–279. https://doi.org/10.1016/j.procs.2016.09.155
Teixeira, J.P., Gonçalves, A., 2014. Accuracy of Jitter and Shimmer Measurements. Procedia Technol. 16, 1190–1199. https://doi.org/10.1016/j.protcy.2014.10.134
Upadhya, S.S., Cheeran, A.N., Nirmal, J.H., 2017. Statistical comparison of Jitter and Shimmer voice features for healthy and Parkinson affected persons. Proc. 2017 2nd IEEE Int. Conf. Electr. Comput. Commun. Technol. ICECCT 2017 1–6. https://doi.org/10.1109/ICECCT.2017.8117853
Wahyuningtyas, V., 2021. Implementasi Ekstraksi Fitur untuk Klasifikasi Suara Urban Menggunakan Deep Learning. Sains, Aplikasi, Komputasi dan Teknologi Informasi, 3(1), pp.10–17.
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