Assessment of response to medication in individuals with Parkinson's disease

Murtadha D. Hssayeni, Michelle A. Burack, Joohi Jimenez-Shahed, Behnaz Ghoraani

Research output: Contribution to journalArticlepeer-review

25 Scopus citations

Abstract

Background and Objective: Motor fluctuations between akinetic (medication OFF) and mobile phases (medication ON) states are one of the most prevalent complications of patients with Parkinson's disease (PD). There is a need for a technology-based system to provide reliable information about the duration in different medication phases that can be used by the treating physician to successfully adjust therapy. Methods: Two KinetiSense motion sensors were mounted on the most affected wrist and ankle of 19 PD subjects (age: 42–77, 14 males) and collected movement signals as the participants performed seven daily living activities in their medication OFF and ON phases. A feature selection and a classification algorithm based on support vector machine with fuzzy labeling was developed to detect medication ON/OFF states using gyroscope signals. The algorithm was trained using approximately 15% of the data from four activities and tested on the remaining data. Results: The algorithm was able to detect medication ON and OFF states with 90.5% accuracy, 94.2% sensitivity, and 85.4% specificity. It performed equally well for all the activities with an average accuracy of 91.3% for the activities that were used in the training phase and 88.4% for the new activities. Conclusions: The developed sensor-based algorithm could provide objective and accurate assessment of medication states that can lead to successful adjustment of the therapy resulting in considerably improved care delivery and quality of life of PD patients.

Original languageEnglish
Pages (from-to)33-43
Number of pages11
JournalMedical Engineering and Physics
Volume67
DOIs
StatePublished - May 2019
Externally publishedYes

Keywords

  • Feature extraction and classification
  • Parkinson's disease
  • Support vector machine
  • Wearable data analysis

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