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NeuroParkNet: A New Neural Network Model for Classification of Parkinson's Disease

Yıl 2025, Erken Görünüm, 1 - 1

Burak Çelik , Ayhan Akbal

Öz

In recent years, the volume and variety of biological data being acquired have increased significantly. Among these data types, the diagnosis of Parkinson's disease holds a critical place in medical research. For this study, speech signals were recorded from patients and healthy controls in a controlled environment at the Neurology Department of Fırat University Hospital. 28 healthy controls, 22 Med Off patients and 30 Med On patients constituted our data set. Participants were asked to read a standardized text in a quiet room using a high-quality H1N Zoom microphone. 19 features were extracted from the obtained sounds. The dataset was categorized into three distinct classes: Healthy Control, Med Off (patients without medication), and Med On (patients medication). To evaluate classification performance, we used a three-layer deeep neural network (DNN) model as well as classical machine learning algorithms in MATLAB. Various classification scenarios have been considered, including many different combinations. For benchmarking, the DNN results were compared with those from commonly used algorithms in the literature: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Naive Bayes (NB). Furthermore, the DNN model’s performance was assessed using the NeuroParkNet architecture. The comparative analysis revealed that the DNN model generally provided a more accurate and efficient classification process. However, in some specific cases, its performance was partially outperformed by traditional classification algorithms. These findings highlight the DNN's potential while also underscoring areas for optimization in Parkinson’s disease classification systems. In addition, the effects of pharmacological treatments were also evaluated in this study.

Anahtar Kelimeler

Parkinson's Disease Speech Analysıs Machine Learning Neural Networks

Kaynakça

  • [1] Feigin, V. L., Nichols, E., Alam, T., Bannick, M. S., Beghi, E., Blake, N., “Global, regional, and national burden of neurological disorders, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016”, The Lancet Neurology, 18(5), 459-480, (2019). DOI: https://doi.org/10.1016/s1474-4422(18)30499-x.
  • [2] Diao, Y., Xie, H., Wang, Y., Zhao, B., Yang, A., Hlavnicka, J., Zhang, J., “Acoustic assessment in mandarin-speaking Parkinson’s disease patients and disease progression monitoring and brain impairment within the speech subsystem”, npj Parkinson's Disease, 10(1), 115, (2024). DOI: https://doi.org/10.1038/s41531-024-00720-3
  • [3] Muñoz-Vigueras, N., Prados-Román, E., Valenza, M. C., Granados-Santiago, M., Cabrera-Martos, I., Rodríguez-Torres, J., Torres-Sánchez, I., “Speech and language therapy treatment on hypokinetic dysarthria in Parkinson disease: Systematic review and meta-analysis”, Clinical Rehabilitation, 35(5), 639-655, (2021). DOI: https://doi.org/10.1177/0269215520976267
  • [4] Arnold, C., Gehrig, J., Gispert, S., Seifried, C., Kell, C. A., “Pathomechanisms and compensatory efforts related to Parkinsonian speech”, NeuroImage: Clinical, 4, 82-97, (2014). DOI: https://doi.org/10.1016/j.nicl.2013.10.016
  • [5] Müller, J., Wenning, G. K., Verny, M., McKee, A., Chaudhuri, K. R., Jellinger, K., Poewe,W., Litvan, I, “Progression of dysarthria and dysphagia in postmortem-confirmed parkinsonian disorders”, Archives of Neurology, 58(2), 259-264, (2001). DOI: https://doi.org/10.1001/archneur.58.2.259
  • [6] Rusz, J., Krack, P., Tripoliti, E., “From prodromal stages to clinical trials: The promise of digital speech biomarkers in Parkinson's disease”, Neuroscience & Biobehavioral Reviews, 105922, (2024). DOI: https://doi.org/10.1016/j.neubiorev.2024.105922
  • [7] Bloem, B. R., Okun, M. S., Klein, C., “Parkinson's disease”, The Lancet, 397(10291), 2284-2303, (2021). DOI: https://doi.org/10.1016/S0140-6736(21)00218-X
  • [8] Sakar, C. O., Serbes, G., Gunduz, A., Tunc, H. C., Nizam, H., Sakar, B. E., Tütüncü, M., Aydin,T., Isenkul, M. E., Apaydin, H., “A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform”, Applied Soft Computing, 74, 255-263, (2019). DOI: https://doi.org/10.1016/j.asoc.2018.10.022
  • [9] Laudis, L. L., Jambek, A. B., A, Lenin Fred., “A Nature Inspired Optimization Algorithm for Parkinson's Disease Classification Through Speech Analysis”, Procedia Computer Science, 235, 840-851, (2024). DOI: https://doi.org/10.1016/j.procs.2024.04.080
  • [10] Tsanas, A., Little, M., McSharry, P., Ramig, L., “Accurate telemonitoring of Parkinson’s disease progression by non-invasive speech tests”, Nature Precedings, 1-1, (2009). DOI: https://doi.org/10.1038/npre.2009.3920.1
  • [11] Taye, M. M., “Understanding of machine learning with deep learning: architectures, workflow, applications and future directions”, Computers, 12(5), 91, (2023). DOI: https://doi.org/10.3390/computers12050091
  • [12] Porumb, M., Stranges, S., Pescapè, A., Pecchia, L., “Precision medicine and artificial intelligence: a pilot study on deep learning for hypoglycemic events detection based on ECG”, Scientific reports, 10(1), 170, (2020). DOI: https://doi.org/10.1038/s41598-019-56927-5.
  • [13] Kadir, T., Gleeson, F., “Lung cancer prediction using machine learning and advanced imaging techniques”, Translational lung cancer research, 7(3), 304, (2018). DOI: https://doi.org/10.21037/tlcr.2018.05.15
  • [14] Saravanan, S., Ramkumar, K., Adalarasu, K., Sivanandam, V., Kumar, S. R., Stalin, S., Amirtharajan, R., “A systematic review of artificial intelligence (AI) based approaches for the diagnosis of Parkinson’s disease”, Archives of computational methods in engineering, 29(6), 3639-3653, (2022). DOI: https://doi.org/10.1007/s11831-022-09710-1
  • [15] Sakar, B. E., Isenkul, M. E., Sakar, C. O., Sertbas, A., Gurgen, F., Delil, S., Kursun, O., “Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings”, IEEE journal of biomedical and health informatics, 17(4), 828-834, (2013). DOI: https://doi.org/10.1109/JBHI.2013.2245674
  • [16] Escobar-Grisales, D., Ríos-Urrego, C. D., Orozco-Arroyave, J. R., “Deep learning and artificial intelligence applied to model speech and language in Parkinson’s disease”, Diagnostics, 13(13), 2163, (2023). DOI: https://doi.org/10.3390/diagnostics13132163.
  • [17] Singh, K. P., Basant, N., Gupta, S., “Support vector machines in water quality management”, Analytica Chimica Acta, 703(2), 152-162, (2011). DOI: https://doi.org/10.1016/j.aca.2011.07.027
  • [18] Loh, H. W., Ooi, C. P., Palmer, E., Barua, P. D., Dogan, S., Tuncer, T., Baygin, M., Acharya, U. R., “GaborPDNet: Gabor transformation and deep neural network for Parkinson’s disease detection using EEG signals”, Electronics, 10(14), 1740, (2021). DOI: https://doi.org/10.3390/electronics10141740
  • [19] Kaplan, E., Altunisik, E., Firat, Y. E., Barua, P. D., Dogan, S., Baygin, M., Demir, F. B., Tuncer, T., Palmer, E., Tan, R.S., Yu, P., Soar, J., Fujita, H., Acharya, U. R., “Novel nested patch-based feature extraction model for automated Parkinson's Disease symptom classification using MRI images”, Computer Methods and Programs in Biomedicine, 224, 107030, (2022). DOI: https://doi.org/10.1016/j.cmpb.2022.107030
  • [20] Tuncer, T., Dogan, S., “A novel octopus based Parkinson’s disease and gender recognition method using vowels”, Applied Acoustics, 155, 75-83, (2019). DOI: https://doi.org/10.1016/j.apacoust.2019.05.019
  • [21] Hossain, M. A., Amenta, F., “Machine learning-based classification of parkinson’s disease patients using speech biomarkers”, Journal of Parkinson’s Disease, 14(1), 95-109, (2024). DOI: https://doi.org/10.3233/JPD-230002
  • [22] Kadhim, M. N., Al-Shammary, D., Sufi, F., “A novel voice classification based on Gower distance for Parkinson disease detection”, International Journal of Medical Informatics, 191, 105583, (2024). DOI: https://doi.org/10.1016/j.ijmedinf.2024.105583
  • [23] Toye, A. A., Kompalli, S., “Comparative study of speech analysis methods to predict parkinson's disease”, arXiv preprint arXiv:2111.10207, (2021). DOI: https://doi.org/10.48550/arXiv.2111.10207
  • [24] Yuan, L., Liu, Y., Feng, H. M., “Parkinson disease prediction using machine learning-based features from speech signal”, Service Oriented Computing and Applications, 18(1), 101-107, (2024). DOI: https://doi.org/10.1007/s11761-023-00372-w
  • [25] Yadav, S., Singh, M. K., Pal, S., “Artificial intelligence model for parkinson disease detection using machine learning algorithms”, Biomedical Materials & Devices, 1(2), 899-911, (2023). DOI: https://doi.org/10.1007/s44174-023-00068-x
  • [26] Sidhu, M. S., Latib, N. A. A., Sidhu, K. K., “MFCC in audio signal processing for voice disorder: a review”, Multimedia Tools and Applications, 1-21, (2024). DOI: https://doi.org/10.1007/s11042-024-19253-1
  • [27] Agarap, A. F., “Deep learning using rectified linear units (relu)”, arXiv preprint arXiv:1803.08375, (2018). DOI: https://doi.org/10.48550/arXiv.1803.08375

Yıl 2025, Erken Görünüm, 1 - 1

Burak Çelik , Ayhan Akbal

Öz

Kaynakça

  • [1] Feigin, V. L., Nichols, E., Alam, T., Bannick, M. S., Beghi, E., Blake, N., “Global, regional, and national burden of neurological disorders, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016”, The Lancet Neurology, 18(5), 459-480, (2019). DOI: https://doi.org/10.1016/s1474-4422(18)30499-x.
  • [2] Diao, Y., Xie, H., Wang, Y., Zhao, B., Yang, A., Hlavnicka, J., Zhang, J., “Acoustic assessment in mandarin-speaking Parkinson’s disease patients and disease progression monitoring and brain impairment within the speech subsystem”, npj Parkinson's Disease, 10(1), 115, (2024). DOI: https://doi.org/10.1038/s41531-024-00720-3
  • [3] Muñoz-Vigueras, N., Prados-Román, E., Valenza, M. C., Granados-Santiago, M., Cabrera-Martos, I., Rodríguez-Torres, J., Torres-Sánchez, I., “Speech and language therapy treatment on hypokinetic dysarthria in Parkinson disease: Systematic review and meta-analysis”, Clinical Rehabilitation, 35(5), 639-655, (2021). DOI: https://doi.org/10.1177/0269215520976267
  • [4] Arnold, C., Gehrig, J., Gispert, S., Seifried, C., Kell, C. A., “Pathomechanisms and compensatory efforts related to Parkinsonian speech”, NeuroImage: Clinical, 4, 82-97, (2014). DOI: https://doi.org/10.1016/j.nicl.2013.10.016
  • [5] Müller, J., Wenning, G. K., Verny, M., McKee, A., Chaudhuri, K. R., Jellinger, K., Poewe,W., Litvan, I, “Progression of dysarthria and dysphagia in postmortem-confirmed parkinsonian disorders”, Archives of Neurology, 58(2), 259-264, (2001). DOI: https://doi.org/10.1001/archneur.58.2.259
  • [6] Rusz, J., Krack, P., Tripoliti, E., “From prodromal stages to clinical trials: The promise of digital speech biomarkers in Parkinson's disease”, Neuroscience & Biobehavioral Reviews, 105922, (2024). DOI: https://doi.org/10.1016/j.neubiorev.2024.105922
  • [7] Bloem, B. R., Okun, M. S., Klein, C., “Parkinson's disease”, The Lancet, 397(10291), 2284-2303, (2021). DOI: https://doi.org/10.1016/S0140-6736(21)00218-X
  • [8] Sakar, C. O., Serbes, G., Gunduz, A., Tunc, H. C., Nizam, H., Sakar, B. E., Tütüncü, M., Aydin,T., Isenkul, M. E., Apaydin, H., “A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform”, Applied Soft Computing, 74, 255-263, (2019). DOI: https://doi.org/10.1016/j.asoc.2018.10.022
  • [9] Laudis, L. L., Jambek, A. B., A, Lenin Fred., “A Nature Inspired Optimization Algorithm for Parkinson's Disease Classification Through Speech Analysis”, Procedia Computer Science, 235, 840-851, (2024). DOI: https://doi.org/10.1016/j.procs.2024.04.080
  • [10] Tsanas, A., Little, M., McSharry, P., Ramig, L., “Accurate telemonitoring of Parkinson’s disease progression by non-invasive speech tests”, Nature Precedings, 1-1, (2009). DOI: https://doi.org/10.1038/npre.2009.3920.1
  • [11] Taye, M. M., “Understanding of machine learning with deep learning: architectures, workflow, applications and future directions”, Computers, 12(5), 91, (2023). DOI: https://doi.org/10.3390/computers12050091
  • [12] Porumb, M., Stranges, S., Pescapè, A., Pecchia, L., “Precision medicine and artificial intelligence: a pilot study on deep learning for hypoglycemic events detection based on ECG”, Scientific reports, 10(1), 170, (2020). DOI: https://doi.org/10.1038/s41598-019-56927-5.
  • [13] Kadir, T., Gleeson, F., “Lung cancer prediction using machine learning and advanced imaging techniques”, Translational lung cancer research, 7(3), 304, (2018). DOI: https://doi.org/10.21037/tlcr.2018.05.15
  • [14] Saravanan, S., Ramkumar, K., Adalarasu, K., Sivanandam, V., Kumar, S. R., Stalin, S., Amirtharajan, R., “A systematic review of artificial intelligence (AI) based approaches for the diagnosis of Parkinson’s disease”, Archives of computational methods in engineering, 29(6), 3639-3653, (2022). DOI: https://doi.org/10.1007/s11831-022-09710-1
  • [15] Sakar, B. E., Isenkul, M. E., Sakar, C. O., Sertbas, A., Gurgen, F., Delil, S., Kursun, O., “Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings”, IEEE journal of biomedical and health informatics, 17(4), 828-834, (2013). DOI: https://doi.org/10.1109/JBHI.2013.2245674
  • [16] Escobar-Grisales, D., Ríos-Urrego, C. D., Orozco-Arroyave, J. R., “Deep learning and artificial intelligence applied to model speech and language in Parkinson’s disease”, Diagnostics, 13(13), 2163, (2023). DOI: https://doi.org/10.3390/diagnostics13132163.
  • [17] Singh, K. P., Basant, N., Gupta, S., “Support vector machines in water quality management”, Analytica Chimica Acta, 703(2), 152-162, (2011). DOI: https://doi.org/10.1016/j.aca.2011.07.027
  • [18] Loh, H. W., Ooi, C. P., Palmer, E., Barua, P. D., Dogan, S., Tuncer, T., Baygin, M., Acharya, U. R., “GaborPDNet: Gabor transformation and deep neural network for Parkinson’s disease detection using EEG signals”, Electronics, 10(14), 1740, (2021). DOI: https://doi.org/10.3390/electronics10141740
  • [19] Kaplan, E., Altunisik, E., Firat, Y. E., Barua, P. D., Dogan, S., Baygin, M., Demir, F. B., Tuncer, T., Palmer, E., Tan, R.S., Yu, P., Soar, J., Fujita, H., Acharya, U. R., “Novel nested patch-based feature extraction model for automated Parkinson's Disease symptom classification using MRI images”, Computer Methods and Programs in Biomedicine, 224, 107030, (2022). DOI: https://doi.org/10.1016/j.cmpb.2022.107030
  • [20] Tuncer, T., Dogan, S., “A novel octopus based Parkinson’s disease and gender recognition method using vowels”, Applied Acoustics, 155, 75-83, (2019). DOI: https://doi.org/10.1016/j.apacoust.2019.05.019
  • [21] Hossain, M. A., Amenta, F., “Machine learning-based classification of parkinson’s disease patients using speech biomarkers”, Journal of Parkinson’s Disease, 14(1), 95-109, (2024). DOI: https://doi.org/10.3233/JPD-230002
  • [22] Kadhim, M. N., Al-Shammary, D., Sufi, F., “A novel voice classification based on Gower distance for Parkinson disease detection”, International Journal of Medical Informatics, 191, 105583, (2024). DOI: https://doi.org/10.1016/j.ijmedinf.2024.105583
  • [23] Toye, A. A., Kompalli, S., “Comparative study of speech analysis methods to predict parkinson's disease”, arXiv preprint arXiv:2111.10207, (2021). DOI: https://doi.org/10.48550/arXiv.2111.10207
  • [24] Yuan, L., Liu, Y., Feng, H. M., “Parkinson disease prediction using machine learning-based features from speech signal”, Service Oriented Computing and Applications, 18(1), 101-107, (2024). DOI: https://doi.org/10.1007/s11761-023-00372-w
  • [25] Yadav, S., Singh, M. K., Pal, S., “Artificial intelligence model for parkinson disease detection using machine learning algorithms”, Biomedical Materials & Devices, 1(2), 899-911, (2023). DOI: https://doi.org/10.1007/s44174-023-00068-x
  • [26] Sidhu, M. S., Latib, N. A. A., Sidhu, K. K., “MFCC in audio signal processing for voice disorder: a review”, Multimedia Tools and Applications, 1-21, (2024). DOI: https://doi.org/10.1007/s11042-024-19253-1
  • [27] Agarap, A. F., “Deep learning using rectified linear units (relu)”, arXiv preprint arXiv:1803.08375, (2018). DOI: https://doi.org/10.48550/arXiv.1803.08375
Toplam 27 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Makine Öğrenme (Diğer)
Bölüm Research Article
Yazarlar

Burak Çelik 0000-0002-3204-5444

Ayhan Akbal 0000-0001-5385-9781

Erken Görünüm Tarihi 26 Nisan 2025
Yayımlanma Tarihi
Gönderilme Tarihi 16 Aralık 2024
Kabul Tarihi 3 Mart 2025
Yayımlandığı Sayı Yıl 2025 Erken Görünüm

Kaynak Göster

APA Çelik, B., & Akbal, A. (2025). NeuroParkNet: A New Neural Network Model for Classification of Parkinson’s Disease. Gazi University Journal of Science1-1.
AMA Çelik B, Akbal A. NeuroParkNet: A New Neural Network Model for Classification of Parkinson’s Disease. Gazi University Journal of Science. Published online 01 Nisan 2025:1-1.
Chicago Çelik, Burak, ve Ayhan Akbal. “NeuroParkNet: A New Neural Network Model for Classification of Parkinson’s Disease”. Gazi University Journal of Science, Nisan (Nisan 2025), 1-1.
EndNote Çelik B, Akbal A (01 Nisan 2025) NeuroParkNet: A New Neural Network Model for Classification of Parkinson’s Disease. Gazi University Journal of Science 1–1.
IEEE B. Çelik ve A. Akbal, “NeuroParkNet: A New Neural Network Model for Classification of Parkinson’s Disease”, Gazi University Journal of Science, ss. 1–1, Nisan 2025.
ISNAD Çelik, Burak - Akbal, Ayhan. “NeuroParkNet: A New Neural Network Model for Classification of Parkinson’s Disease”. Gazi University Journal of Science. Nisan 2025. 1-1.
JAMA Çelik B, Akbal A. NeuroParkNet: A New Neural Network Model for Classification of Parkinson’s Disease. Gazi University Journal of Science. 2025;:1–1.
MLA Çelik, Burak ve Ayhan Akbal. “NeuroParkNet: A New Neural Network Model for Classification of Parkinson’s Disease”. Gazi University Journal of Science, 2025, ss. 1-1.
Vancouver Çelik B, Akbal A. NeuroParkNet: A New Neural Network Model for Classification of Parkinson’s Disease. Gazi University Journal of Science. 2025:1-.