Identification of Alzheimer's EEG With a WVG Network-Based Fuzzy Learning Approach

Yu, Haitao; Zhu, Lin; Cai, Lihui; Wang, Jiang; Liu, Jing; Wang, Ruofan; Zhang, Zhiyong

doi:10.3389/fnins.2020.00641

Cited by 24 publications

(18 citation statements)

References 66 publications

(80 reference statements)

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“…In the first category various machine learning methods, such as k -nearest neighbors ( k -NN) and support-vector machines (SVM), have been adopted [ 11 , 13 , 20 , 21 , 22 ]. The second category, i.e., deep learning-based techniques, includes deep neural networks (DNNs) and in particular recurrent neural networks (RNNs) [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

“…Regarding the deep learning-based techniques, in [ 13 ] authors propose two EEG features, namely, epoch-based entropy (a measure of signal complexity) and bump modeling (a measure of synchrony) and demonstrate that these features are sufficient for efficient discrimination between subjective cognitive impairment (SCI) patients, MCI patients, possible AD patients, and patients with other pathologies, obtaining a classification accuracy of 81.8% to 88.8%. In [ 20 ], a novel analytical framework combining fuzzy learning and complex network approaches has been proposed for the identification of AD with multichannel scalp-recorded EEG signals, obtaining a highest accuracy of 97.12%. In [ 23 , 24 ] authors propose a strategy to use RNN that can handle missing data, which is common in healthcare data.…”

Section: Introductionmentioning

confidence: 99%

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EEG-Based Alzheimer’s Disease Recognition Using Robust-PCA and LSTM Recurrent Neural Network

Alessandrini

Biagetti

Crippa

et al. 2022

Sensors

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The use of electroencephalography (EEG) has recently grown as a means to diagnose neurodegenerative pathologies such as Alzheimer’s disease (AD). AD recognition can benefit from machine learning methods that, compared with traditional manual diagnosis methods, have higher reliability and improved recognition accuracy, being able to manage large amounts of data. Nevertheless, machine learning methods may exhibit lower accuracies when faced with incomplete, corrupted, or otherwise missing data, so it is important do develop robust pre-processing techniques do deal with incomplete data. The aim of this paper is to develop an automatic classification method that can still work well with EEG data affected by artifacts, as can arise during the collection with, e.g., a wireless system that can lose packets. We show that a recurrent neural network (RNN) can operate successfully even in the case of significantly corrupted data, when it is pre-filtered by the robust principal component analysis (RPCA) algorithm. RPCA was selected because of its stated ability to remove outliers from the signal. To demonstrate this idea, we first develop an RNN which operates on EEG data, properly processed through traditional PCA; then, we use corrupted data as input and process them with RPCA to filter outlier components, showing that even with data corruption causing up to 20% erasures, the RPCA was able to increase the detection accuracy by about 5% with respect to the baseline PCA.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EEG-Based Alzheimer’s Disease Recognition Using Robust-PCA and LSTM Recurrent Neural Network

Alessandrini

Biagetti

Crippa

et al. 2022

Sensors

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“…Now, as the value of this parameter increases, we see that other harmonics appear and the main harmonics are pulled to lower powers. In this way, the low-frequency power amplitude increases (Cutsuridis and Moustafa, 2017;Yu et al, 2020).…”

Section: Modeling What Stream Function During Navigation Using Adapti...mentioning

confidence: 94%

A fuzzy-oscillatory model of medial prefrontal cortex control function in spatial memory retrieval in human navigation function

Moghadam

Towhidkhah

Gharibzadeh

2022

Front. Syst. Neurosci.

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Navigation can be broadly defined as the process of moving from an origin to a destination through path-planning. Previous research has shown that navigation is mainly related to the function of the medial temporal lobe (MTL), including the hippocampus (HPC), and medial prefrontal cortex (mPFC), which controls retrieval of the spatial memories from this region. In this study, we suggested a cognitive and computational model of human navigation with a focus on mutual interactions between the hippocampus (HPC) and the mPFC using the concept of synchrony. The Van-der-pol oscillator was used to model the synchronous process of receiving and processing “what stream” information. A fuzzy lookup table system was applied for modeling the controlling function of the mPFC in retrieving spatial information from the HPC. The effect of attention level was also included and simulated. The performance of the model was evaluated using information reported in previous experimental research. Due to the inherent stability of the proposed fuzzy-oscillatory model, it is less sensitive to the exact values of the initial conditions, and therefore, it is shown that it is consistent with the actual human performance in real environments. Analyzing the proposed cognitive and fuzzy-oscillatory computational model demonstrates that the model is able to reproduce certain cognitive and functional disturbances in navigation in related diseases such as Alzheimer’s disease (AD). We have shown that an increase in the bifurcation parameter of the Van-der-pol equation represents an increase in the low-frequency spectral power density and a decrease in the high-frequency spectral power as occurs in AD due to an increase in the amyloid plaques in the brain. These changes in the frequency characteristics of neuronal activity, in turn, lead to impaired recall and retrieval of landmarks information and learned routes upon encountering them. As a result, and because of the wrong frequency code being transmitted, the relevant set of rules in the mPFC is not activated, or another unrelated set will be activated, which leads to forgetfulness and erroneous decisions in routing and eventually losing the route in Alzheimer’s patients.

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“…Furthermore, ML techniques have been used on EEG signals to understand their complex electrophysiological activities and characterize the dynamic features of a complex brain network. Several studies have utilized traditional machine learning models such as the K-nearest neighbor (KNN), decision tree (DT), random forest (RF), Naïve Bayes, and regression models to investigate neurological disorders [ 33 , 34 ]. In a recent study, support vector machines (SVM), KNN, and Naïve Bayes were used to predict the AD [ 32 ] and SVM, correctly classifying 83% of the subjects using network features.…”

Section: Introductionmentioning

confidence: 99%

Age-Related Alterations in EEG Network Connectivity in Healthy Aging

2022

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Emerging studies have reported that functional brain networks change with increasing age. Graph theory is applied to understand the age-related differences in brain behavior and function, and functional connectivity between the regions is examined using electroencephalography (EEG). The effect of normal aging on functional networks and inter-regional synchronization during the working memory (WM) state is not well known. In this study, we applied graph theory to investigate the effect of aging on network topology in a resting state and during performing a visual WM task to classify aging EEG signals. We recorded EEGs from 20 healthy middle-aged and 20 healthy elderly subjects with their eyes open, eyes closed, and during a visual WM task. EEG signals were used to construct the functional network; nodes are represented by EEG electrodes; and edges denote the functional connectivity. Graph theory matrices including global efficiency, local efficiency, clustering coefficient, characteristic path length, node strength, node betweenness centrality, and assortativity were calculated to analyze the networks. We applied the three classifiers of K-nearest neighbor (KNN), a support vector machine (SVM), and random forest (RF) to classify both groups. The analyses showed the significantly reduced network topology features in the elderly group. Local efficiency, global efficiency, and clustering coefficient were significantly lower in the elderly group with the eyes-open, eyes-closed, and visual WM task states. KNN achieved its highest accuracy of 98.89% during the visual WM task and depicted better classification performance than other classifiers. Our analysis of functional network connectivity and topological characteristics can be used as an appropriate technique to explore normal age-related changes in the human brain.

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Identification of Alzheimer's EEG With a WVG Network-Based Fuzzy Learning Approach

Cited by 24 publications

References 66 publications

EEG-Based Alzheimer’s Disease Recognition Using Robust-PCA and LSTM Recurrent Neural Network

EEG-Based Alzheimer’s Disease Recognition Using Robust-PCA and LSTM Recurrent Neural Network

A fuzzy-oscillatory model of medial prefrontal cortex control function in spatial memory retrieval in human navigation function

Age-Related Alterations in EEG Network Connectivity in Healthy Aging

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