scholarly journals Analysis of entropies based on empirical mode decomposition in amnesic mild cognitive impairment of diabetes mellitus

2015 ◽  
Vol 08 (05) ◽  
pp. 1550010 ◽  
Author(s):  
Dong Cui ◽  
Jinhuan Wang ◽  
Zhijie Bian ◽  
Qiuli Li ◽  
Lei Wang ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Empirical Mode Decomposition ◽  
Sensitivity And Specificity ◽  
Cognitive Functions ◽  
Classification Accuracy ◽  
Permutation Entropy ◽  
Support Vector ◽  
Features Selection ◽  
Mode Decomposition

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.

scholarly journals Gearbox Fault Diagnosis Using Complementary Ensemble Empirical Mode Decomposition and Permutation Entropy

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Liye Zhao ◽  
Wei Yu ◽  
Ruqiang Yan
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Correlation Coefficients ◽  
Ensemble Empirical Mode Decomposition ◽  
Permutation Entropy ◽  
Support Vector ◽  
Svm Classifier ◽  
Intrinsic Mode Functions ◽  
Vibration Signals ◽  
Mode Decomposition

This paper presents an improved gearbox fault diagnosis approach by integrating complementary ensemble empirical mode decomposition (CEEMD) with permutation entropy (PE). The presented approach identifies faults appearing in a gearbox system based on PE values calculated from selected intrinsic mode functions (IMFs) of vibration signals decomposed by CEEMD. Specifically, CEEMD is first used to decompose vibration signals characterizing various defect severities into a series of IMFs. Then, filtered vibration signals are obtained from appropriate selection of IMFs, and correlation coefficients between the filtered signal and each IMF are used as the basis for useful IMFs selection. Subsequently, PE values of those selected IMFs are utilized as input features to a support vector machine (SVM) classifier for characterizing the defect severity of a gearbox. Case study conducted on a gearbox system indicates the effectiveness of the proposed approach for identifying the gearbox faults.

scholarly journals Fugl-Meyer hand motor imagination recognition for brain–computer interfaces using only fNIRS

2021 ◽  
Author(s):  
Chenguang Li ◽  
Hongjun Yang ◽  
Long Cheng
Keyword(s):  
Classification Accuracy ◽  
Near Infrared ◽  
Support Vector ◽  
Classification Methods ◽  
Experimental Paradigm ◽  
Functional Near Infrared Spectroscopy ◽  
Machine Method ◽  
Physiological Signal ◽  
Computer Interfaces ◽  
Mode Decomposition

AbstractAs a relatively new physiological signal of brain, functional near-infrared spectroscopy (fNIRS) is being used more and more in brain–computer interface field, especially in the task of motor imagery. However, the classification accuracy based on this signal is relatively low. To improve the accuracy of classification, this paper proposes a new experimental paradigm and only uses fNIRS signals to complete the classification task of six subjects. Notably, the experiment is carried out in a non-laboratory environment, and movements of motion imagination are properly designed. And when the subjects are imagining the motions, they are also subvocalizing the movements to prevent distraction. Therefore, according to the motor area theory of the cerebral cortex, the positions of the fNIRS probes have been slightly adjusted compared with other methods. Next, the signals are classified by nine classification methods, and the different features and classification methods are compared. The results show that under this new experimental paradigm, the classification accuracy of 89.12% and 88.47% can be achieved using the support vector machine method and the random forest method, respectively, which shows that the paradigm is effective. Finally, by selecting five channels with the largest variance after empirical mode decomposition of the original signal, similar classification results can be achieved.

scholarly journals Validation of Indian Council of Medical Research Neurocognitive Tool Box in Diagnosis of Mild Cognitive Impairment in India: Lessons from a Harmonization Process in a Linguistically Diverse Society

2021 ◽  
pp. 1-10
Author(s):  
Ramshekhar N. Menon ◽  
Feba Varghese ◽  
Avanthi Paplikar ◽  
Shailaja Mekala ◽  
Suvarna Alladi ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Medical Research ◽  
Sensitivity And Specificity ◽  
Linguistically Diverse ◽  
Z Score ◽  
Indian Languages ◽  
Test Protocol ◽  
Indian Council ◽  
Multiple Domain

<b><i>Background/Aims:</i></b> In a linguistically diverse country such as India, challenges remain with regard to diagnosis of early cognitive decline among the elderly, with no prior attempts made to simultaneously validate a comprehensive battery of tests across domains in multiple languages. This study aimed to determine the utility of the Indian Council of Medical Research-Neurocognitive Tool Box (ICMR-NCTB) in the diagnosis of mild cognitive impairment (MCI) and its vascular subtype (VaMCI) in 5 Indian languages. <b><i>Methods:</i></b> Literate subjects from 5 centers across the country were recruited using a uniform process, and all subjects were classified based on clinical evaluations and a gold standard test protocol into normal cognition, MCI, and VaMCI. Following adaptation and harmonization of the ICMR-NCTB across 5 different Indian languages into a composite Z score, its test performance against standards, including sensitivity and specificity of the instrument as well as of its subcomponents in diagnosis of MCI, was evaluated in age and education unmatched and matched groups. <b><i>Results:</i></b> Variability in sensitivity-specificity estimates was noted between languages when a total of 991 controls and 205 patients with MCI (157 MCI and 48 VaMCI) were compared due to a significant impact of age, education, and language. Data from a total of 506 controls, 144 patients with MCI, and 46 patients with VaMCI who were age- and education-matched were compared. Post hoc analysis after correction for multiple comparisons revealed better performance in controls relative to all-cause MCI. An optimum composite Z-score of −0.541 achieved a sensitivity of 81.1% and a specificity of 88.8% for diagnosis of all-cause MCI, with a high specificity for diagnosis of VaMCI. Using combinations of multiple-domain 2 test subcomponents retained a sensitivity and specificity of &#x3e;80% for diagnosis of MCI. <b><i>Conclusions:</i></b> The ICMR-NCTB is a “first of its kind” approach at harmonizing neuropsychological tests across 5 Indian languages for the diagnosis of MCI due to vascular and other etiologies. Utilizing multiple-domain subcomponents also retains the validity of this instrument, making it a valuable tool in MCI research in multilingual settings.

scholarly journals Classification accuracy of TMS for the diagnosis of mild cognitive impairment

2021 ◽  
Vol 14 (2) ◽  
pp. 241-249
Author(s):  
Alberto Benussi ◽  
Mario Grassi ◽  
Fernando Palluzzi ◽  
Valentina Cantoni ◽  
Maria Sofia Cotelli ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Classification Accuracy

scholarly journals Multimodal measurement approach to identify individuals with mild cognitive impairment: study protocol for a cross-sectional trial

BMJ Open ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. e046879
Author(s):  
Bernhard Grässler ◽  
Fabian Herold ◽  
Milos Dordevic ◽  
Tariq Ali Gujar ◽  
Sabine Darius ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Statistical Analysis ◽  
Cognitive Decline ◽  
Cognitive Performance ◽  
Classification Accuracy ◽  
Multimodal Approach ◽  
Cross Sectional ◽  
Age Related ◽  
Age Related Cognitive Decline

IntroductionThe diagnosis of mild cognitive impairment (MCI), that is, the transitory phase between normal age-related cognitive decline and dementia, remains a challenging task. It was observed that a multimodal approach (simultaneous analysis of several complementary modalities) can improve the classification accuracy. We will combine three noninvasive measurement modalities: functional near-infrared spectroscopy (fNIRS), electroencephalography and heart rate variability via ECG. Our aim is to explore neurophysiological correlates of cognitive performance and whether our multimodal approach can aid in early identification of individuals with MCI.Methods and analysisThis study will be a cross-sectional with patients with MCI and healthy controls (HC). The neurophysiological signals will be measured during rest and while performing cognitive tasks: (1) Stroop, (2) N-back and (3) verbal fluency test (VFT). Main aims of statistical analysis are to (1) determine the differences in neurophysiological responses of HC and MCI, (2) investigate relationships between measures of cognitive performance and neurophysiological responses and (3) investigate whether the classification accuracy can be improved by using our multimodal approach. To meet these targets, statistical analysis will include machine learning approaches.This is, to the best of our knowledge, the first study that applies simultaneously these three modalities in MCI and HC. We hypothesise that the multimodal approach improves the classification accuracy between HC and MCI as compared with a unimodal approach. If our hypothesis is verified, this study paves the way for additional research on multimodal approaches for dementia research and fosters the exploration of new biomarkers for an early detection of nonphysiological age-related cognitive decline.Ethics and disseminationEthics approval was obtained from the local Ethics Committee (reference: 83/19). Data will be shared with the scientific community no more than 1 year following completion of study and data assembly.Trial registration numberClinicalTrials.gov, NCT04427436, registered on 10 June 2020, https://clinicaltrials.gov/ct2/show/study/NCT04427436.

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