Implementation of Artifact Removal Algorithms in Gait Signals for Diagnosis of Parkinson Disease
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Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Int Information & Engineering Technology Assoc
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Parkinson's disease (PD) is a neurological disease that progresses further over time. Individuals suffering from this condition have a deficiency of dopamine, a neurotransmitter found in the brain's nerve cells that is critical for coordinating body movement. In this study, a new approach is proposed for the diagnosis of PD. Common Average Reference (CAR), Median Common Average Reference (MCAR), and Weighted Common Average Reference (WCAR) methods were primarily utilized to eliminate noise from the multichannel recorded walking signals in the resulting PhysioNet dataset. Statistical features were obtained from the clean walking signals following the Local Binary Pattern (LBP) transformation application. Logistic Regression (LR), Random Forest (RF), and K-nearest neighbor (Kim) methods were utilized in the classification stage. A high success rate with a value of 92.96% was observed with Kim. It was also determined that signals on which foot and the signals obtained from which point of the sole of the foot were effective in PD diagnosis in the study. In light of the findings, it was observed that noise reduction methods increased the success rate of PD diagnosis.
Açıklama
Anahtar Kelimeler
filtering and noise reduction, Parkinson disease, feature extraction, signal processing
Kaynak
Traitement Du Signal
WoS Q Değeri
Q3
Scopus Q Değeri
N/A
Cilt
38
Sayı
3
