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Med Eng Phys. 2019 May;67:33-43. doi: 10.1016/j.medengphy.2019.03.002. Epub 2019 Mar 12.

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

Author information

1
Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA.
2
Department of Neurology, University of Rochester Medical Center, Rochester, NY 14642, USA.
3
Department of Neurology, Baylor College of Medicine, Houston, TX, USA.
4
Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL 33431, USA. Electronic address: bghoraani@ieee.org.

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.

KEYWORDS:

Feature extraction and classification; Parkinson’s disease; Support vector machine; Wearable data analysis

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