RECURRENCE QUANTIFICATION ANALYSIS OF MCI EEG UNDER RESTING AND VISUAL MEMORY TASK CONDITIONS
| Autor: | Leena T. Timothy, Usha Nair, Bindu M. Krishna |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
0303 health sciences
medicine.medical_specialty medicine.diagnostic_test Computer science Biomedical Engineering Biophysics Bioengineering Electroencephalography Audiology Task (project management) 03 medical and health sciences 0302 clinical medicine Visual memory Recurrence quantification analysis medicine Cognitive impairment Normal control 030217 neurology & neurosurgery 030304 developmental biology |
| Zdroj: | Biomedical Engineering: Applications, Basis and Communications. 31:1950025 |
| ISSN: | 1793-7132 1016-2372 |
| Popis: | The work aims at classifying EEG of mild cognitive impairment (MCI) patients from that of normal control (NC) subjects using recurrence quantification analysis (RQA) and a simple visual memory task, which is commonly used in memory clinics. EEG of MCI and NC groups are recorded under three cognitive conditions, resting eyes closed (EC) and two phases of the task, namely, picture viewing (learning phase, PIC) and picture recollection (immediate free recall phase, PICREC). Complexity analysis of EEG is performed using RQA measures, recurrence rate (RR) and entropy (ENTR). Mean values of these measures over electrodes from four cortical regions are used for statistical analysis of group differences, under the different cognitive conditions. In all the cortical regions, the mean RQA RR and ENTR values of MCI group are observed to be higher compared to NC group under the task conditions. Receiver operating characteristics (ROC) analysis is used for assessing the classification efficiency of the RQA-based method applied to EEG of MCI subjects. A fair classification is obtained in all the four cortical regions during the PIC condition using RR and in all regions except frontal, using ENTR. In the PICREC condition, a good classification is obtained in the temporal, parietal and occipital regions and a fair classification is attained in the frontal region using RR. In this condition, the ENTR values provided a fair classification in all the four cortical regions. These RQA measures are used as feature vectors of SVM classifier to further confirm the classification efficiency of the couplets of RQA RR and ENTR. These results indicate RQA method can efficiently classify MCI EEG based on complexity levels using the simple immediate free recall task. |
| Databáze: | OpenAIRE |
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