MO32 Effect of intensified metabolic control on cognitive performance and EEG in patients with type 2 diabetes

Coupling feature extraction method of resting state EEG Signals from amnestic mild cognitive impairment with type 2 diabetes mellitus based on weight permutation conditional mutual information

This study aimed to find a good coupling feature extraction method to effectively analyze resting state EEG signals (rsEEG) of amnestic mild cognitive impairment(aMCI) with type 2 diabetes mellitus(T2DM) and normal control (NC) with T2DM. A method of EEG signal coupling feature extraction based on weight permutation conditional mutual information (WPCMI) was proposed in this research. With the WPCMI method, coupling feature strength of two time series in Alpha1, Alpha2, Beta1, Beta2 and Gamma bands for aMCI with T2DM and NC with T2DM could be extracted respectively. Then selected three frequency bands coupling feature matrix with the help of multi-spectral image transformation method to map it as spectral image characteristics. And finally classified these characteristics through the convolution neural network method(CNN). For aMCI with T2DM and NC with T2DM, the highest classification accuracy of 96%, 95%, 95% could be achieved respectively in the combination of three frequency bands (Alpha1, Alpha2, Gamma), (Beta1, Beta2 and Gamma) and (Alpha2, Beta1, Beta2). This WPCMI method highlighted the coupling dynamic characteristics of EEG signals, and its classification performance was better than all previous methods in aMCI with T2DM diagnosis field. WPCMI method could be used as an effective biomarker to distinguish EEG signals of aMCI with T2DM and NC with T2DM. The coupling feature extraction method used in this paper provided a new perspective for the EEG analysis of aMCI with T2DM.

Feature Classification Method of Resting-State EEG Signals From Amnestic Mild Cognitive Impairment With Type 2 Diabetes Mellitus Based on Multi-View Convolutional Neural Network

The convolutional neural network (CNN) model is an active research topic in the field of EEG signals analysis. However, the classification effect of CNN on EEG signals of amnestic mild cognitive impairment (aMCI) with type 2 diabetes mellitus (T2DM) is not ideal. Even if EEG signals are transformed into multispectral images that are more closely matched with the model, the best classification performance can not be achieved. Therefore, to improve the performance of CNN toward EEG multispectral image classification, a multi-view convolutional neural network (MVCNN) classification model based on inceptionV1 is designed in this study. This model mainly improves and optimizes the convolutional layers and stochastic gradient descent (SGD) in the convolutional architecture model. Firstly, based on the discreteness of EEG multispectral image features, the multi-view convolutional layer structure was proposed. Then the learning rate change function of the SGD was optimized to increase the classification performance. The multi-view convolutional nerve was used in an EEG multispectral classification task involving 19 aMCI with T2DM and 20 normal controls. The results showed that compared with the traditional classification models, MVCNN had a better stability and accuracy. Therefore, MVCNN could be used as an effective feature classification method for aMCI with T2DM.

Resting-state EEG signal classification of amnestic mild cognitive impairment with type 2 diabetes mellitus based on multispectral image and convolutional neural network

OBJECTIVE

The purpose of this study is to judge whether this combination method of multispectral image and convolutional neural network (CNN) method can be used to distinguish amnestic mild cognitive impairment (aMCI) with Type 2 diabetes mellitus (T2DM) and normal controls (NC) with T2DM effectively.

APPROACH

In this study, the authors first combined EEG signals from aMCI patients with T2DM and NC with T2DM on five different frequency bands, including Theta, Alpha1, Alpha2, Beta1, and Beta2. Then, the authors converted these time series into a series of multispectral images. Finally, the images data were classified with the CNN method.

MAIN RESULTS

The classification effects of up to 89%, 91%, and 92% are obtained on the three combinations of frequency bands: Theta, Alpha1, and Alpha2; Alpha1, Alpha2, and Beta1; and Alpha2, Beta1, and Beta2. The spatial properties of EEG signals are highlighted, and its classification performance is found to be better than all the previous methods in the field of aMCI and T2DM diagnosis. The combination of multispectral images and CNN can be used as an effective biomarker for distinguishing the EEG signals in patients with aMCI and T2DM and in patients with NC with T2DM.

SIGNIFICANCE

The combined approach used in this paper provides a new perspective for the analysis of EEG signals in patients with aMCI and T2DM.

The feature extraction of resting-state EEG signal from amnestic mild cognitive impairment with type 2 diabetes mellitus based on feature-fusion multispectral image method

Recently, combining feature extraction and classification method of electroencephalogram (EEG) signals has been widely used in identifying mild cognitive impairment. However, it remains unclear which feature of EEG signals is best effective in assessing amnestic mild cognitive impairment (aMCI) with type 2 diabetes mellitus (T2DM) when combining one classifier. This study proposed a novel feature extraction method of EEG signals named feature-fusion multispectral image method (FMIM) for diagnosis of aMCI with T2DM. The FMIM was integrated with convolutional neural network (CNN) to classify the processed multispectral image data. The results showed that FMIM could effectively identify aMCI with T2DM from the control group compared to existing multispectral image method (MIM), with improvements including the type and quantity of feature extraction. Meanwhile, part of the invalid calculation could be avoided during the classification process. In addition, the classification evaluation indexes were best under the combination of Alpha2-Beta1-Beta2 frequency bands in data set based on FMIM-1, and were also best under the combination of the Theta-Alpha1-Alpha2-Beta1-Beta2 frequency bands in data set based on FMIM-2. Therefore, FMIM can be used as an effective feature extraction method of aMCI with T2DM, and as a valuable biomarker in clinical applications.

Resting-state EEG coupling analysis of amnestic mild cognitive impairment with type 2 diabetes mellitus by using permutation conditional mutual information

OBJECTIVE

This study was meant to explore whether the coupling strength and direction of resting-state electroencephalogram (rsEEG) could be used as an indicator to distinguish the patients of type 2 diabetes mellitus (T2DM) with or without amnestic mild cognitive impairment (aMCI).

METHODS

Permutation conditional mutual information (PCMI) was used to calculate the coupling strength and direction of rsEEG signals between different brain areas of 19 aMCI and 20 normal control (NC) with T2DM on 7 frequency bands: Delta, Theta, Alpha1, Alpha2, Beta1, Beta2 and Gamma. The difference in coupling strength or direction of rsEEG between two groups was calculated. The correlation between coupling strength or direction of rsEEG and score of different neuropsychology scales were also calculated.

RESULTS

We have demonstrated that PCMI can calculate effectively the coupling strength and directionality of EEG signals between different brain regions. The significant difference in coupling strength and directionality of EEG signals was found between the patients of aMCI and NC with T2DM on different brain regions. There also existed significant correlation between sex or age and coupling strength or coupling directionality of EEG signals between a few different brain regions from all subjects.

CONCLUSIONS

The coupling strength or directionality of EEG signals calculated by PCMI are significantly different between aMCI and NC with T2DM.

SIGNIFICANCE

These results showed that the coupling strength or directionality of EEG signals calculated by PCMI might be used as a biomarker in distinguishing the aMCI from NC with T2DM.