Wearable-based Mediation State Detection in Individuals with Parkinson's Disease
This addresses the need for objective measures to help physicians adjust therapy for individuals with Parkinson's disease, though it is incremental as it applies an existing method to a new application.
The paper tackled the problem of unreliable self-reporting for medication state detection in Parkinson's disease patients by developing a wearable-based algorithm using motion sensors and a support vector machine with fuzzy labeling, achieving an average classification accuracy of 90.5%, sensitivity of 94.2%, and specificity of 85.4%.
One of the most prevalent complaints of individuals with mid-stage and advanced Parkinson's disease (PD) is the fluctuating response to their medication (i.e., ON state with maximum benefit from medication and OFF state with no benefit from medication). In order to address these motor fluctuations, the patients go through periodic clinical examination where the treating physician reviews the patients' self-report about duration in different medication states and optimize therapy accordingly. Unfortunately, the patients' self-report can be unreliable and suffer from recall bias. There is a need to a technology-based system that can provide objective measures about the duration in different medication states that can be used by the treating physician to successfully adjust the therapy. In this paper, we developed a medication state detection algorithm to detect medication states using two wearable motion sensors. A series of significant features are extracted from the motion data and used in a classifier that is based on a support vector machine with fuzzy labeling. The developed algorithm is evaluated using a dataset with 19 PD subjects and a total duration of 1,052.24 minutes (17.54 hours). The algorithm resulted in an average classification accuracy of 90.5%, sensitivity of 94.2%, and specificity of 85.4%.