Complexity Analysis of Electroencephalogram Dynamics in Patients with Parkinson’s Disease
In this study, a new combination scheme has been proposed for detecting Parkinson’s disease (PD) from electroencephalogram (EEG) signal recorded from normal subjects and PD patients. The scheme is based on discrete wavelet transform (DWT), sample entropy (SampEn), and the three-way decision model in...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Parkinson's Disease |
| Online Access: | http://dx.doi.org/10.1155/2017/8701061 |
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| _version_ | 1849412752223764480 |
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| author | Guotao Liu Yanping Zhang Zhenghui Hu Xiuquan Du Wanqing Wu Chenchu Xu Xiangyang Wang Shuo Li |
| author_facet | Guotao Liu Yanping Zhang Zhenghui Hu Xiuquan Du Wanqing Wu Chenchu Xu Xiangyang Wang Shuo Li |
| author_sort | Guotao Liu |
| collection | DOAJ |
| description | In this study, a new combination scheme has been proposed for detecting Parkinson’s disease (PD) from electroencephalogram (EEG) signal recorded from normal subjects and PD patients. The scheme is based on discrete wavelet transform (DWT), sample entropy (SampEn), and the three-way decision model in analysis of EEG signal. The EEG signal is noisy and nonstationary, and, as a consequence, it becomes difficult to distinguish it visually. However, the scheme is a well-established methodology in analysis of EEG signal in three stages. In the first stage, the DWT was applied to acquire the split frequency information; here, we use three-level DWT to decompose EEG signal into approximation and detail coefficients; in this stage, we aim to remove the useless and noise information and acquire the effective information. In the second stage, as the SampEn has advantage in analyzing the EEG signal, we use the approximation coefficient to compute the SampEn values. Finally, we detect the PD patients using three-way decision based on optimal center constructive covering algorithm (O_CCA) with the accuracy about 92.86%. Without DWT as preprocessing step, the detection rate reduces to 88.10%. Overall, the combination scheme we proposed is suitable and efficient in analyzing the EEG signal with higher accuracy. |
| format | Article |
| id | doaj-art-54b4e8bf63c94286a508be6f2a94a90b |
| institution | Kabale University |
| issn | 2090-8083 2042-0080 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Parkinson's Disease |
| spelling | doaj-art-54b4e8bf63c94286a508be6f2a94a90b2025-08-20T03:34:21ZengWileyParkinson's Disease2090-80832042-00802017-01-01201710.1155/2017/87010618701061Complexity Analysis of Electroencephalogram Dynamics in Patients with Parkinson’s DiseaseGuotao Liu0Yanping Zhang1Zhenghui Hu2Xiuquan Du3Wanqing Wu4Chenchu Xu5Xiangyang Wang6Shuo Li7School of Computer Science and Technology, Anhui University, Hefei 230601, ChinaSchool of Computer Science and Technology, Anhui University, Hefei 230601, ChinaCenter for Optics and Optoelectronics Research, College of Science, Zhejiang University of Technology, Pingfeng Campus, Liuhe Road 288, Xihu District, Hangzhou 310023, ChinaSchool of Computer Science and Technology, Anhui University, Hefei 230601, ChinaShenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaSchool of Computer Science and Technology, Anhui University, Hefei 230601, ChinaAnhui Electrical Engineering Professional Technique College, Hefei 230051, ChinaDepartment of Medical Imaging, Schulich School of Medicine and Dentistry, University of Western Ontario, 1151 Richmond St, London, ON, CanadaIn this study, a new combination scheme has been proposed for detecting Parkinson’s disease (PD) from electroencephalogram (EEG) signal recorded from normal subjects and PD patients. The scheme is based on discrete wavelet transform (DWT), sample entropy (SampEn), and the three-way decision model in analysis of EEG signal. The EEG signal is noisy and nonstationary, and, as a consequence, it becomes difficult to distinguish it visually. However, the scheme is a well-established methodology in analysis of EEG signal in three stages. In the first stage, the DWT was applied to acquire the split frequency information; here, we use three-level DWT to decompose EEG signal into approximation and detail coefficients; in this stage, we aim to remove the useless and noise information and acquire the effective information. In the second stage, as the SampEn has advantage in analyzing the EEG signal, we use the approximation coefficient to compute the SampEn values. Finally, we detect the PD patients using three-way decision based on optimal center constructive covering algorithm (O_CCA) with the accuracy about 92.86%. Without DWT as preprocessing step, the detection rate reduces to 88.10%. Overall, the combination scheme we proposed is suitable and efficient in analyzing the EEG signal with higher accuracy.http://dx.doi.org/10.1155/2017/8701061 |
| spellingShingle | Guotao Liu Yanping Zhang Zhenghui Hu Xiuquan Du Wanqing Wu Chenchu Xu Xiangyang Wang Shuo Li Complexity Analysis of Electroencephalogram Dynamics in Patients with Parkinson’s Disease Parkinson's Disease |
| title | Complexity Analysis of Electroencephalogram Dynamics in Patients with Parkinson’s Disease |
| title_full | Complexity Analysis of Electroencephalogram Dynamics in Patients with Parkinson’s Disease |
| title_fullStr | Complexity Analysis of Electroencephalogram Dynamics in Patients with Parkinson’s Disease |
| title_full_unstemmed | Complexity Analysis of Electroencephalogram Dynamics in Patients with Parkinson’s Disease |
| title_short | Complexity Analysis of Electroencephalogram Dynamics in Patients with Parkinson’s Disease |
| title_sort | complexity analysis of electroencephalogram dynamics in patients with parkinson s disease |
| url | http://dx.doi.org/10.1155/2017/8701061 |
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