Analysis of entropies based on empirical mode decomposition in amnesic mild cognitive impairment of diabetes mellitus
EEG characteristics that correlate with the cognitive functions are important in detecting mild cognitive impairment (MCI) in T2DM. To investigate the complexity between aMCI group and age-matched non-aMCI control group in T2DM, six entropies combining empirical mode decomposition (EMD), including A...
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2015-09-01
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doaj-8db075991efe49eaa212641a4a7d16b62020-11-24T20:59:17ZengWorld Scientific PublishingJournal of Innovative Optical Health Sciences1793-54581793-72052015-09-01851550010-11550010-2010.1142/S179354581550010810.1142/S1793545815500108Analysis of entropies based on empirical mode decomposition in amnesic mild cognitive impairment of diabetes mellitusDong Cui0Jinhuan Wang1Zhijie Bian2Qiuli Li3Lei Wang4Xiaoli Li5School of Information Science and Engineering, Yanshan University, Qinhuangdao, P. R. ChinaSchool of Information Science and Engineering, Yanshan University, Qinhuangdao, P. R. ChinaSchool of Electrical Engineering, Yanshan University, Qinhuangdao, P. R. ChinaDepartment of Neurology, General Hospital of Second Artillery Corps of PLA, Beijing, P. R. ChinaDepartment of Neurology, General Hospital of Second Artillery Corps of PLA, Beijing, P. R. ChinaSchool of Electrical Engineering, Yanshan University, Qinhuangdao, P. R. ChinaEEG characteristics that correlate with the cognitive functions are important in detecting mild cognitive impairment (MCI) in T2DM. To investigate the complexity between aMCI group and age-matched non-aMCI control group in T2DM, six entropies combining empirical mode decomposition (EMD), including Approximate entropy (ApEn), Sample entropy (SaEn), Fuzzy entropy (FEn), Permutation entropy (PEn), Power spectrum entropy (PsEn) and Wavelet entropy (WEn) were used in the study. A feature extraction technique based on maximization of the area under the curve (AUC) and a support vector machine (SVM) were subsequently used to for features selection and classification. Finally, Pearson's linear correlation was employed to study associations between these entropies and cognitive functions. Compared to other entropies, FEn had a higher classification accuracy, sensitivity and specificity of 68%, 67.1% and 71.9%, respectively. Top 43 salient features achieved classification accuracy, sensitivity and specificity of 73.8%, 72.3% and 77.9%, respectively. P4, T4 and C4 were the highest ranking salient electrodes. Correlation analysis showed that FEn based on EMD was positively correlated to memory at electrodes F7, F8 and P4, and PsEn based on EMD was positively correlated to Montreal cognitive assessment (MoCA) and memory at electrode T4. In sum, FEn based on EMD in right-temporal and occipital regions may be more suitable for early diagnosis of the MCI with T2DM.http://www.worldscientific.com/doi/pdf/10.1142/S1793545815500108Entropyempirical mode decompositionamnestic mild cognitive impairmenttype 2 diabetes mellitus |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Dong Cui Jinhuan Wang Zhijie Bian Qiuli Li Lei Wang Xiaoli Li |
spellingShingle |
Dong Cui Jinhuan Wang Zhijie Bian Qiuli Li Lei Wang Xiaoli Li Analysis of entropies based on empirical mode decomposition in amnesic mild cognitive impairment of diabetes mellitus Journal of Innovative Optical Health Sciences Entropy empirical mode decomposition amnestic mild cognitive impairment type 2 diabetes mellitus |
author_facet |
Dong Cui Jinhuan Wang Zhijie Bian Qiuli Li Lei Wang Xiaoli Li |
author_sort |
Dong Cui |
title |
Analysis of entropies based on empirical mode decomposition in amnesic mild cognitive impairment of diabetes mellitus |
title_short |
Analysis of entropies based on empirical mode decomposition in amnesic mild cognitive impairment of diabetes mellitus |
title_full |
Analysis of entropies based on empirical mode decomposition in amnesic mild cognitive impairment of diabetes mellitus |
title_fullStr |
Analysis of entropies based on empirical mode decomposition in amnesic mild cognitive impairment of diabetes mellitus |
title_full_unstemmed |
Analysis of entropies based on empirical mode decomposition in amnesic mild cognitive impairment of diabetes mellitus |
title_sort |
analysis of entropies based on empirical mode decomposition in amnesic mild cognitive impairment of diabetes mellitus |
publisher |
World Scientific Publishing |
series |
Journal of Innovative Optical Health Sciences |
issn |
1793-5458 1793-7205 |
publishDate |
2015-09-01 |
description |
EEG characteristics that correlate with the cognitive functions are important in detecting mild cognitive impairment (MCI) in T2DM. To investigate the complexity between aMCI group and age-matched non-aMCI control group in T2DM, six entropies combining empirical mode decomposition (EMD), including Approximate entropy (ApEn), Sample entropy (SaEn), Fuzzy entropy (FEn), Permutation entropy (PEn), Power spectrum entropy (PsEn) and Wavelet entropy (WEn) were used in the study. A feature extraction technique based on maximization of the area under the curve (AUC) and a support vector machine (SVM) were subsequently used to for features selection and classification. Finally, Pearson's linear correlation was employed to study associations between these entropies and cognitive functions. Compared to other entropies, FEn had a higher classification accuracy, sensitivity and specificity of 68%, 67.1% and 71.9%, respectively. Top 43 salient features achieved classification accuracy, sensitivity and specificity of 73.8%, 72.3% and 77.9%, respectively. P4, T4 and C4 were the highest ranking salient electrodes. Correlation analysis showed that FEn based on EMD was positively correlated to memory at electrodes F7, F8 and P4, and PsEn based on EMD was positively correlated to Montreal cognitive assessment (MoCA) and memory at electrode T4. In sum, FEn based on EMD in right-temporal and occipital regions may be more suitable for early diagnosis of the MCI with T2DM. |
topic |
Entropy empirical mode decomposition amnestic mild cognitive impairment type 2 diabetes mellitus |
url |
http://www.worldscientific.com/doi/pdf/10.1142/S1793545815500108 |
work_keys_str_mv |
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