Volume 14, Issue 2 (June 2018)                   IJEEE 2018, 14(2): 106-115 | Back to browse issues page

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Rezghian Moghadam H, Kobravi H R, Homam M. Quantification of Parkinson Tremor Intensity Based On EMG Signal Analysis Using Fast Orthogonal Search Algorithm. IJEEE. 2018; 14 (2) :106-115
URL: http://ijeee.iust.ac.ir/article-1-1133-en.html
Abstract:   (2344 Views)
The tremor injury is one of the common symptoms of Parkinsonchr('39')s disease. The patients suffering from Parkinsonchr('39')s disease have difficulty in controlling their movements owing to tremor. The intensity of the disease can be determined through specifying the range of intensity values of involuntary tremor in Parkinson patients. The level of disease in patients is determined through an empirical range of 0-5. In the early stages of Parkinson, resting tremor can be very mild and intermittent. So, diagnosing the levels of disease is difficult but important since it has only medication therapy. The aim of this study is to quantify the intensity of tremor by the analysis of electromyogram signal. The solution proposed in this paper is to employ a polynomial function model to estimate the Unified Parkinsonchr('39')s Disease Rating Scale (UPDRS) value. The algorithm of Fast Orthogonal Search (FOS), which is based on identification of orthogonal basic functions, was utilized for model identification. In fact, some linear and nonlinear features extracted from wrist surface electromyogram signal were considered as the input of the model identified by FOS, and the model output was the UPDRS value. In this research, the proposed model was designed based on two different structures which have been called the single structure and parallel structure. The efficiency of designed models with different structures was evaluated. The evaluation results using K-fold cross validation approach showed that the proposed model with a parallel structure could determine the tremor severity of the Parkinsonchr('39')s disease with accuracy of 99.25% ±0.41, sensitivity of 97.17% ±1.9 and specificity of 99.72% ±0.18.
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Type of Study: Research Paper | Subject: Biomedical Signal Processing
Received: 2017/07/16 | Revised: 2018/06/17 | Accepted: 2018/01/08

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