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User-Independent Detection for Freezing of Gait in Parkinson's Disease Using Random Forest Classification

User-Independent Detection for Freezing of Gait in Parkinson's Disease Using Random Forest Classification

Amruta Meshram, Bharatendra Rai
Copyright: © 2021 |Pages: 17
ISBN13: 9781799834410|ISBN10: 1799834417|EISBN13: 9781799834427
DOI: 10.4018/978-1-7998-3441-0.ch023
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MLA

Meshram, Amruta, and Bharatendra Rai. "User-Independent Detection for Freezing of Gait in Parkinson's Disease Using Random Forest Classification." Research Anthology on Diagnosing and Treating Neurocognitive Disorders, edited by Information Resources Management Association, IGI Global, 2021, pp. 456-472. https://doi.org/10.4018/978-1-7998-3441-0.ch023

APA

Meshram, A. & Rai, B. (2021). User-Independent Detection for Freezing of Gait in Parkinson's Disease Using Random Forest Classification. In I. Management Association (Ed.), Research Anthology on Diagnosing and Treating Neurocognitive Disorders (pp. 456-472). IGI Global. https://doi.org/10.4018/978-1-7998-3441-0.ch023

Chicago

Meshram, Amruta, and Bharatendra Rai. "User-Independent Detection for Freezing of Gait in Parkinson's Disease Using Random Forest Classification." In Research Anthology on Diagnosing and Treating Neurocognitive Disorders, edited by Information Resources Management Association, 456-472. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-3441-0.ch023

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Abstract

Freezing of gait (FOG) is a gait impairment which occurs in Parkinson's disease (PD) patients. As PD progresses, the patient is unable to perform locomotion normally. This increases the risk of falls and adversely affects the patient's quality of life. In this article, a user-independent method has been proposed to detect FOG events in PD patients. The proposed method is divided into three phases. Phase-1 extracts the statistical features from a FOG dataset. Phase-2 divides the data into two clusters based on FOG events. Phase-3 selects significant factors, using a randomized block design with replication. A Random Forest model is built using a combination of significant factors obtained from the design of experiments. The proposed method classifies FOG events with an average sensitivity up to 94.33% and specificity up to 92.77%. This model can be integrated along with non-pharmaceutical treatments to generate sensory-motor feedback at the onset of a FOG event.

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