Table_5_Hand Resting Tremor Assessment of Healthy and Patients With Parkinson’s Disease: An Exploratory Machine Learning Study.DOCX (17.69 kB)

Table_5_Hand Resting Tremor Assessment of Healthy and Patients With Parkinson’s Disease: An Exploratory Machine Learning Study.DOCX

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posted on 2020-07-14, 04:52 authored by Ana Camila Alves de Araújo, Enzo Gabriel da Rocha Santos, Karina Santos Guedes de Sá, Viviane Kharine Teixeira Furtado, Felipe Augusto Santos, Ramon Costa de Lima, Lane Viana Krejcová, Bruno Lopes Santos-Lobato, Gustavo Henrique Lima Pinto, André dos Santos Cabral, Anderson Belgamo, Bianca Callegari, Ana Francisca Rozin Kleiner, Anselmo de Athayde Costa e Silva, Givago da Silva Souza

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.