Implementation of Artifact Removal Algorithms in Gait Signals for Diagnosis of Parkinson Disease

نویسندگان

چکیده

Parkinson's disease (PD) is a neurological that progresses further over time. Individuals suffering from this condition have deficiency of dopamine, neurotransmitter found in the brain's nerve cells critical for coordinating body movement. In study, new approach proposed diagnosis PD. Common Average Reference (CAR), Median (MCAR), and Weighted (WCAR) methods were primarily utilized to eliminate noise multichannel recorded walking signals resulting PhysioNet dataset. Statistical features obtained clean following Local Binary Pattern (LBP) transformation application. Logistic Regression (LR), Random Forest (RF), K-nearest neighbor (Knn) classification stage. A high success rate with value 92.96% was observed Knn. It also determined on which foot point sole effective PD study. light findings, it reduction increased diagnosis.

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ژورنال

عنوان ژورنال: Traitement Du Signal

سال: 2021

ISSN: ['0765-0019', '1958-5608']

DOI: https://doi.org/10.18280/ts.380306