Hand Resting Tremor Assessment of Healthy and Patients With Parkinson’s Disease: An Exploratory Machine Learning Study

Frontiers in Bioengineering and Biotechnology

Ana Francisca Rozin Kleiner¹  André dos Santos Cabral²  Anderson Belgamo³  Givago da Silva Souza⁴  Ramon Costa de Lima⁴  Enzo Gabriel da Rocha Santos⁵  Gustavo Henrique Lima Pinto⁵  Lane Viana Krejcová⁶  Felipe Augusto Santos⁷  Anselmo de Athayde Costa e Silva⁷  Bianca Callegari⁷  Karina Santos Guedes de Sá⁷  Bruno Lopes Santos-Lobato⁷  Viviane Kharine Teixeira Furtado⁹  Ana Camila Alves de Araújo^1,10 

[1] 0Departamento de Fisioterapia, Universidade Federal de São Carlos, São Carlos, Brazil;Centro de Ciências Biológicas e da Saúde, Universidade do Estado do Pará, Belém, Brazil;Departamento de Ciência da Computação, Instituto Federal de São Paulo, Piracicaba, Brazil;Instituto de Ciências Biológicas, Universidade Federal do Pará, Belém, Brazil;Instituto de Ciências Exatas e Naturais, Universidade Federal do Pará, Belém, Brazil;Instituto de Ciências da Arte, Universidade Federal do Pará, Belém, Brazil;Instituto de Ciências da Saúde, Universidade Federal do Pará, Belém, Brazil;Laboratório Rainha Sílvia de Análise do Movimento, Rio Claro, Brazil;Núcleo de Medicina Tropical, Universidade Federal do Pará, Belém, Brazil;Núcleo de Teoria e Pesquisa do Comportamento, Universidade Federal do Pará, Belém, Brazil;

关键词: Parkinson’s disease;    inertial sensors;    accelerometer;    gyroscope;    hand resting tremor;    machine learning;   

DOI  :  10.3389/fbioe.2020.00778

来源: DOAJ

【 摘 要 】

The aim of this study is comparing the accuracies of machine learning algorithms to classify data concerning healthy subjects and patients with Parkinson’s Disease (PD), toward different time window lengths and a number of features. Thirty-two healthy subjects and eighteen patients with PD took part on this study. The study obtained inertial recordings by using an accelerometer and a gyroscope assessing both hands of the subjects during hand resting state. We extracted time and temporal frequency domain features to feed seven machine learning algorithms: k-nearest-neighbors (kNN); logistic regression; support vector classifier (SVC); linear discriminant analysis; random forest; decision tree; and gaussian Naïve Bayes. The accuracy of the classifiers was compared using different numbers of extracted features (i.e., 272, 190, 136, 82, and 27) from different time window lengths (i.e., 1, 5, 10, and 15 s). The inertial recordings were characterized by oscillatory waveforms that, especially in patients with PD, peaked in a frequency range between 3 and 8 Hz. Outcomes showed that the most important features were the mean frequency, linear prediction coefficients, power ratio, power density skew, and kurtosis. We observed that accuracies calculated in the testing phase were higher than in the training phase. Comparing the testing accuracies, we found significant interactions among time window length and the type of classifier (p < 0.05). The study found significant effects on estimated accuracies, according to their type of algorithm, time window length, and their interaction. kNN presented the highest accuracy, while SVC showed the worst results. kNN feeding by features extracted from 1 and 5 s were the combination with more frequently highest accuracies. Classification using few features led to similar decision of the algorithms. Moreover, performance increased significantly according to the number of features used, reaching a plateau around 136. Finally, the results of this study suggested that kNN was the best algorithm to classify hand resting tremor in patients with PD.

【 授权许可】

Unknown