A Wearable Multi-Segment Upper Limb Tremor Assessment System for Differential Diagnosis of Parkinson’s Disease Versus Essential Tremor

Abstract

Upper limb tremor is a prominent symptom of both Parkinson’s disease and essential tremor. Its kinematic parameters overlap substantially for these two pathological conditions, thus leading to high rate of misdiagnosis, especially for community doctors. Several groups have proposed various methods for improving differential diagnosis. These prior studies have attempted to identify better kinematic parameters, however they have mainly focused on single limb features including tremor intensity, tremor frequency, and tremor variability. In this paper, we propose a wearable system for multi-segment assessment of upper limb tremor and differential diagnosis of Parkinson’s disease versus essential tremor. The proposed system collected tremor data from both wrist and fingers simultaneously. From this data, we extracted multi-segment features in the form of phase relationships between limb segments. Using support vector machine classifiers, we then performed differential diagnosis from the extracted features. We evaluated the performance of the proposed system on 19 Parkinson’s disease patients and 12 essential tremor patients. Moreover, we also assessed the performance cost associated with reducing task load and sensor array size. The proposed system reached perfect accuracy in leave-one-out cross validation. Task reduction and sensor array reduction were associated with penalties of 2% and 9-10% respectively. The results demonstrated that the proposed system could be simplified for clinical applications, and successfully applied to the differential diagnosis of Parkinson’s disease versus essential tremor in real-world setting.

Keywords

• Parkinson’s Disease
• essential tremor
• tremor
• multi-segment
• machine learning