High-accuracy automatic classification of Parkinsonian tremor severity using machine learning method

Although clinical aspirations for new technology to accurately measure and diagnose Parkinsonian tremors exist, automatic scoring of tremor severity using machine learning approaches has not yet been employed.

This study aims to maximize the scientific validity of automatic tremor-severity classification using machine learning algorithms to score Parkinsonian tremor severity in the same manner as the unified Parkinson's disease rating scale (UPDRS) used to rate scores in real clinical practice.

Eighty-five PD patients perform four tasks for severity assessment of their resting, resting with mental stress, postural, and intention tremors. The tremor signals are measured using a wristwatch-type wearable device with an accelerometer and gyroscope. Displacement and angle signals are obtained by integrating the acceleration and angular-velocity signals. Nineteen features are extracted from each of the four tremor signals. The optimal feature configuration is decided using the wrapper feature selection algorithm or principal component analysis, and decision tree, support vector machine, discriminant analysis, and k-nearest neighbour algorithms are considered to develop an automatic scoring system for UPDRS prediction. The results are compared to UPDRS ratings assigned by two neurologists.

The highest accuracies are 92.3%, 86.2%, 92.1%, and 89.2% for resting, resting with mental stress, postural, and intention tremors, respectively. The weighted Cohen's kappa values are 0.745, 0.635 and 0.633 for resting, resting with mental stress, and postural tremors (almost perfect agreement), and 0.570 for intention tremors (moderate).

These results indicate the feasibility of the proposed system as a clinical decision tool for Parkinsonian tremor-severity automatic scoring.