A Dementia Classification Framework Using Frequency and Time-Frequency Features Based on EEG Signals

Durongbhan, Pholpat; Zhao, Yifan; Chen, Liang‐Yu; Zis, Panagiotis; Marco, Matteo De; Unwin, Zoe C.; Venneri, Annalena; He, Xiongxiong; Li, Sheng; Zhao, Yitian; Blackburn, Daniel; Sarrigiannis, Ptolemaios G.

doi:10.1109/tnsre.2019.2909100

Cited by 85 publications

(52 citation statements)

References 31 publications

(45 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…More sophisticated quantitative EEG analysis with fractal dimension, an approach thought to be related to the complexity of EEG signal dynamics, has demonstrated significant change in the complexity of electrical neuronal activity throughout the lifespan, with a steady increase in young and middle-aged adults followed by a fall in the elderly [40]. In addition, previous work using various EEG features and a rigorous ML framework [36], estimated fairly reliably the participants chronological age exclusively based on brain electrical recordings. In this work we show that for linear synchronisation, HC participants below the age of 70 have clear differences in the strength of synchronisation between EO and EC states, stronger and more widespread in the posterior quadrant brain areas in the latter state.…”

Section: Discussionmentioning

confidence: 99%

“…The proposed classification using machine learning approaches based on features from ERR connectivity values, was developed as an add-on to the previously proposed dementia classification framework [36]. As already fine-tuned and implemented in the original framework, K-Nearest Neighbour (KNN) classification method where K=1 with 10fold cross-validation was used in this paper.…”

Section: F Classification Using Machine Learningmentioning

confidence: 99%

See 1 more Smart Citation

Imaging of Nonlinear and Dynamic Functional Brain Connectivity Based on EEG Recordings With the Application on the Diagnosis of Alzheimer’s Disease

Zhao

Durongbhan

et al. 2020

IEEE Trans. Med. Imaging

Self Cite

View full text Add to dashboard Cite

Since age is the most significant risk factor for the development of Alzheimer's disease (AD), it is important to understand the effect of normal ageing on brain network characteristics before we can accurately diagnose the condition based on information derived from resting state electroencephalogram (EEG) recordings, aiming to detect brain network disruption. This paper proposes a novel brain functional connectivity imaging method, particularly targeting the contribution of nonlinear dynamics of functional connectivity, on distinguishing participants with AD from healthy controls (HC). We describe a parametric method established upon a Nonlinear Finite Impulse Response model, and a revised orthogonal least squares algorithm used to estimate the linear, nonlinear and combined connectivity between any two EEG channels without fitting a full model. This approach, where linear and non-linear interactions and their spatial distribution and dynamics can be estimated independently, offered us the means to dissect the dynamic brain network disruption in AD from a new perspective and to gain some insight into the dynamic behaviour of brain networks in two age groups (above and below 70) with normal cognitive function. Although linear and stationary connectivity dominates the classification contributions, quantitative results have demonstrated that nonlinear and dynamic connectivity can significantly improve the classification

show abstract

Section: Discussionmentioning

confidence: 99%

Section: F Classification Using Machine Learningmentioning

confidence: 99%

Imaging of Nonlinear and Dynamic Functional Brain Connectivity Based on EEG Recordings With the Application on the Diagnosis of Alzheimer’s Disease

Zhao

Durongbhan

et al. 2020

IEEE Trans. Med. Imaging

Self Cite

View full text Add to dashboard Cite

show abstract

“…It should be able to learn and map effectively. Related scholars have proposed a regenerative space framework for information theory learning [10]- [12]. The framework uses a symmetric non-negative definite kernel function, the potential for cross information.…”

Section: Introductionmentioning

confidence: 99%

Medical Health Big Data Classification Based on KNN Classification Algorithm

2020

View full text Add to dashboard Cite

The rapid development of information technology has led to the development of medical informatization in the direction of intelligence. Medical health big data provides a basic data resource guarantee for medical service intelligence and smart healthcare. The classification of medical health big data is of great significance for the intelligentization of medical information. Due to the simplicity of KNN (K-Nearest Neighbor) classification algorithm, it has been widely used in many fields. However, when the sample size is large and the feature attributes are large, the efficiency of the KNN algorithm classification will be greatly reduced. This paper proposes an improved KNN algorithm and compares it with the traditional KNN algorithm. The classification is performed in the query instance neighborhood of the conventional KNN classifier, and weights are assigned to each class. The algorithm considers the class distribution around the query instance to ensure that the assigned weight does not adversely affect the outliers. Aiming at the shortcomings of traditional KNN algorithm in processing large data sets, this paper proposes an improved KNN algorithm based on cluster denoising and density cropping. The algorithm performs denoising processing by clustering, and improves the classification efficiency of KNN algorithm by speeding up the search speed of K-nearest neighbors, while maintaining the classification accuracy of KNN algorithm. The experimental results show that the proposed algorithm can effectively improve the classification efficiency of KNN algorithm in processing large data sets, and maintain the classification accuracy of KNN algorithm well, and has good classification performance. INDEX TERMS Improved KNN classifier, weighted KNN algorithm, cluster denoising, density cropping.

show abstract

“…Wu et al [5] combined CSP and multivariate empirical mode decomposition to classify left-and right-hand MI. Durongbhan et al [6] extracted different features through wavelet transform (WT) and FFT to classify Alzheimer's disease.…”

Section: Introductionmentioning

confidence: 99%

EEG Feature Extraction Based on a Bilevel Network: Minimum Spanning Tree and Regional Network

Luo

2020

Electronics

View full text Add to dashboard Cite

Feature extraction is essential for classifying different motor imagery (MI) tasks in a brain–computer interface (BCI). Although the methods of brain network analysis have been widely studied in the BCI field, these methods are limited by differences in network size, density, and standardization. To address this issue and improve classification accuracy, we propose a novel method, in which the hybrid features of the brain function based on the bilevel network are extracted. Minimum spanning tree (MST) based on electroencephalogram (EEG) signal nodes in different MIs is constructed as the first network layer to solve the global network connectivity problem. In addition, the regional network in different movement patterns is constructed as the second network layer to determine the network characteristics, which is consistent with the correspondence between limb movement patterns and cerebral cortex in neurophysiology. We attempt to apply MST to the classification of the MI EEG signals, and the bilevel network has better interpretability. Thereafter, a vector is formed by combining the MST fundamental features with the directional features of the regional network. Our method is validated using the BCI Competition IV Dataset I. Experimental results verify the feasibility of the bilevel network framework. Furthermore, the average classification performance of the proposed method reaches 89.50%, which is higher than that of other competing methods, thereby indicating that the bilevel network is effective for MI classification.

show abstract

A Dementia Classification Framework Using Frequency and Time-Frequency Features Based on EEG Signals

Cited by 85 publications

References 31 publications

Imaging of Nonlinear and Dynamic Functional Brain Connectivity Based on EEG Recordings With the Application on the Diagnosis of Alzheimer’s Disease

Imaging of Nonlinear and Dynamic Functional Brain Connectivity Based on EEG Recordings With the Application on the Diagnosis of Alzheimer’s Disease

Medical Health Big Data Classification Based on KNN Classification Algorithm

EEG Feature Extraction Based on a Bilevel Network: Minimum Spanning Tree and Regional Network

Contact Info

Product

Resources

About