sapiСистемный анализ и прикладная информатика«System analysis and applied information science»2309-49232414-0481Belarusian National Technical University10.21122/2309-4923-2023-4-51-57sapi-645Research ArticleОбработка информации и принятие решенийData processing and decision–makingИТ-диагностика болезни Паркинсона на основе голосовых маркеров и снижения двигательной активностиIT diagnostics of Parkinson's disease based on voice markers and decreased motor activityВишняковВ. И.VishniakouU. V.

Вишняков Владимир Анатольевич, доктор технических наук, профессор, профессор кафедры инфокоммуникационных технологий

Минск

Vishnyakou Uladzimir Anatolyevich, Doctor of Technical Sciences, Professor, Professor of the Department of Information and Communication Technologies

Minsk

vish@bsuir.byИвэйСяYiweiX.

Ся Ивэй, магистр технических наук, аспирант кафедры ИКТ БГУИР

Минск

Xia Yiwei, master of technical science, PhD-student of ICT department

Minsk

Белорусский государственный университет информатики и радиоэлектроникиБеларусьBelarusian state University of Informatics and RadioelectronicsBelarus202312012024045157Copyright © Вишняков В.И., Ивэй С., 20242024Вишняков В.И., Ивэй С.Vishniakou U.V., Yiwei X.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://sapi.bntu.by/jour/article/view/645

Цели статьи ‒ предложить метод комплексного распознавания болезни Паркинсона с использованием машинного обучения, основанный на анализе маркеров голоса и изменений в движениях пациента на известных наборах данных. Используются частотно-временная функция (вейвлет-функция) и функция коэффициента Мейера Кепстраля. Алгоритм KNN и алгоритм двухслойной нейронной сети были использованы для обучения и тестирования на общедоступных наборах данных об изменениях речи и замедлении движений при болезни Паркинсона. Байесовский оптимизатор также использовался для улучшения гиперпараметров алгоритма KNN. Построенные модели достигли точности 94,7 % и 96,2 % для набора данных об изменениях речи у пациентов с болезнью Паркинсона и набора данных о замедлении передвижения пациентов, соответственно. Результаты распознавания близки к мировому уровню. Предлагаемая методика предназначена для использования в подсистеме ИТ-диагностики нервных заболеваний.

The objectives of the article to propose the method for complex recognition of Parkinson's disease using machine learning, based on markers of voice analysis and changes in patient movements on known data sets. The time-frequency function, (the wavelet function) and the Meyer kepstral coefficient function are used. The KNN algorithm and the algorithm of a two-layer neural network were used for training and testing on publicly available datasets on speech changes and motion retardation in Parkinson's disease. A Bayesian optimizer was also used to improve the hyperparameters of the KNN algorithm. The constructed models achieved an accuracy of 94.7 % and 96.2  % on a data set on speech changes in patients with Parkinson's disease and a data set on slowing down the movement of patients, respectively. The recognition results are close to the world level. The proposed technique is intended for use in the subsystem of IT diagnostics of nervous diseases.

распознавание болезни Паркинсонамашинное обучениеалгоритм KNNбайесовская нейронная сетьParkinsons disease recognitionmachine learningKNN algorithmBayesian neural networkReferencesDavie, C.A. A review of Parkinson’s disease. Br. Med. Bull., Feb. 2008, vol. 86, no. 1, pp. 109-127. doi: 10.1093/bmb/ldn013Davie, C.A. A review of Parkinson’s disease. Br. Med. Bull., Feb. 2008, vol. 86, no. 1, pp. 109-127. doi: 10.1093/bmb/ldn013Braak, H., Ghebremedhin, E. Rüb U. Stages in the development of Parkinson’s disease-related pathology. Cell Tissue Res., Oct. 2004, vol. 318, no. 1, pp. 121-134. doi: 10.1007/s00441-004-0956-9Braak, H., Ghebremedhin, E. Rüb U. Stages in the development of Parkinson’s disease-related pathology. Cell Tissue Res., Oct. 2004, vol. 318, no. 1, pp. 121-134. doi: 10.1007/s00441-004-0956-9Upadhya, S.S., Cheeran A.N. Discriminating Parkinson and Healthy People Using Phonation and Cepstral Features of Speech. Procedia Comput. Sci., Jan. 2018, vol. 143, pp. 197-202. doi: 10.1016/j.procs.2018.10.376Upadhya, S.S., Cheeran A.N. Discriminating Parkinson and Healthy People Using Phonation and Cepstral Features of Speech. Procedia Comput. Sci., Jan. 2018, vol. 143, pp. 197-202. doi: 10.1016/j.procs.2018.10.376Xu, Z. [et al.] Voiceprint recognition of Parkinson patients based on deep learning. arXiv, Dec., 2018, pp. 1-10. doi: 10.48550/arXiv.1812.06613Xu, Z. [et al.] Voiceprint recognition of Parkinson patients based on deep learning. arXiv, Dec., 2018, pp. 1-10. doi: 10.48550/arXiv.1812.06613Arshad, H. [et. al.] Multi-level features fusion and selection for human gait recognition: an optimized framework of Bayesian model and binomial distribution. Int. J. Mach. Learn. and Cybern., Dec., 2018, vol. 10, no. 12, pp. 3601-3618. doi: 10.1007/s13042-019-00947-0Arshad, H. [et. al.] Multi-level features fusion and selection for human gait recognition: an optimized framework of Bayesian model and binomial distribution. Int. J. Mach. Learn. and Cybern., Dec., 2018, vol. 10, no. 12, pp. 3601-3618. doi: 10.1007/s13042-019-00947-0Maachi I.E., Bilodeau G.-A., Bouachir W. Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait. Expert Syst. Appl., May 2020, vol. 143, pp. 1-27. doi: 10.1016/j.eswa.2019.113075Maachi I.E., Bilodeau G.-A., Bouachir W. Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait. Expert Syst. Appl., May 2020, vol. 143, pp. 1-27. doi: 10.1016/j.eswa.2019.113075Moro-Velazquez, L. [et. al.] Advances in Parkinson’s Disease detection and assessment using voice and speech: A review of the articulatory and phonatory aspects. Biomed. Signal Process. Control, Apr. 2021, vol. 66, pp. 1-13. doi: 10.1016/j.bspc.2021.102418Moro-Velazquez, L. [et. al.] Advances in Parkinson’s Disease detection and assessment using voice and speech: A review of the articulatory and phonatory aspects. Biomed. Signal Process. Control, Apr. 2021, vol. 66, pp. 1-13. doi: 10.1016/j.bspc.2021.102418Nguyen, D.M. [et. al.] Transformers for 1D Signals in Parkinson’s Disease Detection from Gait. arXiv, Apr., 2022, pp. 1-7. doi: 10.48550/arXiv.2204.00423Nguyen, D.M. [et. al.] Transformers for 1D Signals in Parkinson’s Disease Detection from Gait. arXiv, Apr., 2022, pp. 1-7. doi: 10.48550/arXiv.2204.00423Zhang, M.-L., Zhou Z.-H. ML-KNN: A lazy learning approach to multi-label learning, Pattern Recognit., 2007, vol. 40, no. 7, pp. 1-21.Zhang, M.-L., Zhou Z.-H. ML-KNN: A lazy learning approach to multi-label learning, Pattern Recognit., 2007, vol. 40, no. 7, pp. 1-21.Arshad, H. [et. al.] Multi-level features fusion and selection for human gait recognition: an optimized framework of Bayesian model and binomial distribution. Int. J. Mach. Learn. & Cyber, 2019, vol. 10 (12), pp. 3601-3618.Arshad, H. [et. al.] Multi-level features fusion and selection for human gait recognition: an optimized framework of Bayesian model and binomial distribution. Int. J. Mach. Learn. & Cyber, 2019, vol. 10 (12), pp. 3601-3618.Sakar C.O. [et al.] A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform. Appl. Soft Comput., Jan. 2019, vol. 74, pp. 255-263. doi: 10.1016/j.asoc.2018.10.022Sakar C.O. [et al.] A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform. Appl. Soft Comput., Jan. 2019, vol. 74, pp. 255-263. doi: 10.1016/j.asoc.2018.10.022Fawcett, T. An introduction to ROC analysis. Pattern Recognit. Lett. 2006, vol. 27, no. 8, pp. 861-874.Fawcett, T. An introduction to ROC analysis. Pattern Recognit. Lett. 2006, vol. 27, no. 8, pp. 861-874.Sakar B.E., [et al.] Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE J Biomed Health Inform, 2013, vol. 17(4), pp. 828-834.Sakar B.E., [et al.] Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE J Biomed Health Inform, 2013, vol. 17(4), pp. 828-834.Li, B., Yao Z., Wang J., Wang S., Yang X., Sun Y. Improved Deep Learning Technique to Detect Freezing of Gait in Parkinson’s Disease Based on Wearable Sensors. Electronics, 2020, no 9 (11), pp. 1-12.Li, B., Yao Z., Wang J., Wang S., Yang X., Sun Y. Improved Deep Learning Technique to Detect Freezing of Gait in Parkinson’s Disease Based on Wearable Sensors. Electronics, 2020, no 9 (11), pp. 1-12.

The authors declare that there are no conflicts of interest present.