Abstract
BackgroundParkinson’s disease (PD) and essential tremor (ET) are movement disorders that can have similar clinical characteristics including tremor and gait difficulty. These disorders can be misdiagnosed leading to delay in appropriate treatment. The aim of the study was to determine whether balance and gait variables obtained with wearable inertial motion sensors can be utilized to differentiate between PD and ET using machine learning. Additionally, we compared classification performances of several machine learning models.MethodsThis retrospective study included balance and gait variables collected during the instrumented stand and walk test from people with PD (n = 524) and with ET (n = 43). Performance of several machine learning techniques including neural networks, support vector machine, k-nearest neighbor, decision tree, random forest, and gradient boosting, were compared with a dummy model or logistic regression using F1-scores.ResultsMachine learning models classified PD and ET based on balance and gait characteristics better than the dummy model (F1-score = 0.48) or logistic regression (F1-score = 0.53). The highest F1-score was 0.61 of neural network, followed by 0.59 of gradient boosting, 0.56 of random forest, 0.55 of support vector machine, 0.53 of decision tree, and 0.49 of k-nearest neighbor.ConclusionsThis study demonstrated the utility of machine learning models to classify different movement disorders based on balance and gait characteristics collected from wearable sensors. Future studies using a well-balanced data set are needed to confirm the potential clinical utility of machine learning models to discern between PD and ET.
Highlights
Parkinson’s disease (PD) and essential tremor (ET) are movement disorders that can have similar clinical characteristics including tremor and gait difficulty
With Synthetic Minority Oversampling Technique (SMOTE), (1) the accuracy of the models ranged from 0.65 to 0.89 (NN); (2) the precision was similar across the models ranging from 0.54 (SVM, k-nearest neighbor (kNN), decision tree (DT), and logistic regression (LR)) to 0.61 (NN); (3) the recall ranged from 0.58 (DT) to 0.63; and (4) the F1-score ranged from 0.53 (DT and LR) to 0.61 (NN)
The results without the oversampling approach can be found in Supplementary Table 2. This data-driven study aimed to differentiate between two movement disorders, PD and ET based on balance and gait characteristics collected from inertial motion unit (IMU) sensors using various machine learning models
Summary
Parkinson’s disease (PD) and essential tremor (ET) are movement disorders that can have similar clinical characteristics including tremor and gait difficulty These disorders can be misdiagnosed leading to delay in appropriate treatment. ET has traditionally been considered a mono-symptomatic disorder presenting with tremor, increasing evidence suggests that ET is a complex disorder with involvement of other motor and non-motor symptoms [2] Both PD and ET can share clinical features including motor symptoms such as bradykinesia (slow movement), gait impairment and dystonia (involuntary muscle contraction), and non-motor symptoms such as cognitive impairments, sleep disturbances, depression, and anxiety [3, 4]. Since misdiagnosis can prevent or delay appropriate medical care and worsen patients’ quality of life, accurate differentiation between PD and ET is important to provide optimal care
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