A New Framework for Automatic Detection of Patients With Mild Cognitive Impairment Using Resting-State EEG Signals

Mild cognitive impairment (MCI) can be an indicator representing the early stage of Alzheimier’s disease (AD). AD, which is the most common form of dementia, is a major public health problem worldwide. Efficient detection of MCI is essential to identify the risks of AD and dementia. Currently Electroencephalography (EEG) is the most popular tool to investigate the presenence of MCI biomarkers. This study aims to develop a new framework that can use EEG data to automatically distinguish MCI patients from healthy control subjects. The proposed framework consists of noise removal (baseline drift and power line interference noises), segmentation, data compression, feature extraction, classification, and performance evaluation. This study introduces Piecewise Aggregate Approximation (PAA) for compressing massive volumes of EEG data for reliable analysis. Permutation entropy (PE) and auto-regressive (AR) model features are investigated to explore whether the changes in EEG signals can effectively distinguish MCI from healthy control subjects. Finally, three models are developed based on three modern machine learning techniques: Extreme Learning Machine (ELM); Support Vector Machine (SVM) and K-Nearest Neighbours (KNN) for the obtained feature sets. Our developed models are tested on a publicly available MCI EEG database and the robustness of our models is evaluated by using a 10-fold cross validation method. The results show that the proposed ELM based method achieves the highest classification accuracy (98.78%) with lower execution time (0.281 seconds) and also outperforms the existing methods. The experimental results suggest that our proposed framework could provide a robust biomarker for efficient detection of MCI patients.

[1]  Bashir I. Morshed,et al.  A Single-Channel EEG-Based Approach to Detect Mild Cognitive Impairment via Speech-Evoked Brain Responses , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[2]  Xiongxiong He,et al.  A Dementia Classification Framework Using Frequency and Time-Frequency Features Based on EEG Signals , 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[3]  Hua Wang,et al.  EEG Sleep Stages Analysis and Classification Based on Weighed Complex Network Features , 2021, IEEE Transactions on Emerging Topics in Computational Intelligence.

[4]  Zhenhu Liang,et al.  Parameter selection in permutation entropy for an electroencephalographic measure of isoflurane anesthetic drug effect , 2013, Journal of Clinical Monitoring and Computing.

[5]  Richard J. Kryscio,et al.  Spectral and complexity analysis of scalp EEG characteristics for mild cognitive impairment and early Alzheimer's disease , 2014, Comput. Methods Programs Biomed..

[6]  B. Pompe,et al.  Permutation entropy: a natural complexity measure for time series. , 2002, Physical review letters.

[7]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[8]  Yan Li,et al.  A novel statistical algorithm for multiclass EEG signal classification , 2014, Eng. Appl. Artif. Intell..

[9]  Qingshan She,et al.  Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization , 2016, Comput. Math. Methods Medicine.

[10]  M. H. Kolekar,et al.  EEG and Cognitive Biomarkers Based Mild Cognitive Impairment Diagnosis , 2019, IRBM.

[11]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[12]  William P. Marnane,et al.  Dynamic, location-based channel selection for power consumption reduction in EEG analysis , 2012, Comput. Methods Programs Biomed..

[13]  Yanchun Zhang,et al.  Weighted Visibility Graph With Complex Network Features in the Detection of Epilepsy , 2016, IEEE Access.

[14]  Yan Li,et al.  Improving the Separability of Motor Imagery EEG Signals Using a Cross Correlation-Based Least Square Support Vector Machine for Brain–Computer Interface , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[15]  Yanchun Zhang,et al.  Exploring Douglas-Peucker Algorithm in the Detection of Epileptic Seizure from Multicategory EEG Signals , 2019, BioMed research international.

[16]  Jinli Cao,et al.  An Integrated MCI Detection Framework Based on Spectral-temporal Analysis , 2019, International Journal of Automation and Computing.

[17]  Chris Lynch,et al.  World Alzheimer Report 2019: Attitudes to dementia, a global survey , 2020 .

[18]  Yanchun Zhang,et al.  Exploring Sampling in the Detection of Multicategory EEG Signals , 2015, Comput. Math. Methods Medicine.

[19]  Yan Li,et al.  Designing a robust feature extraction method based on optimum allocation and principal component analysis for epileptic EEG signal classification , 2015, Comput. Methods Programs Biomed..

[20]  Abdulrahman Al-Ahmari,et al.  Anomaly detection using piecewise aggregate approximation in the amplitude domain , 2018, Applied Intelligence.

[21]  Jing Li,et al.  Dynamic characteristics of absence EEG recordings with multiscale permutation entropy analysis , 2013, Epilepsy Research.

[22]  Yan Li,et al.  Epileptic EEG signal classification using optimum allocation based power spectral density estimation , 2018, IET Signal Process..

[23]  Liu Xiao-feng,et al.  Fine-grained permutation entropy as a measure of natural complexity for time series , 2009 .

[24]  Yanchun Zhang,et al.  An Advanced Analysis System for Identifying Alcoholic Brain State Through EEG Signals , 2019, International Journal of Automation and Computing.

[25]  Hossein Rabbani,et al.  Automatic Diagnosis of Mild Cognitive Impairment Using Electroencephalogram Spectral Features , 2016, Journal of medical signals and sensors.

[26]  Kaleb McDowell,et al.  Detection and classification of subject-generated artifacts in EEG signals using autoregressive models , 2012, Journal of Neuroscience Methods.

[27]  Yanchun Zhang,et al.  Classification of THz pulse signals using two-dimensional cross-correlation feature extraction and non-linear classifiers , 2016, Comput. Methods Programs Biomed..

[28]  Quanzheng Li,et al.  Early Diagnosis of Alzheimer's Disease Based on Resting-State Brain Networks and Deep Learning , 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[29]  Damodar Reddy Edla,et al.  Classification of EEG Data using k-Nearest Neighbor approach for Concealed Information Test , 2018 .

[30]  Jiankun Hu,et al.  Deep Gaussian Mixture-Hidden Markov Model for Classification of EEG Signals , 2018, IEEE Transactions on Emerging Topics in Computational Intelligence.

[31]  Yanhui Xu,et al.  Classification Based on Multilayer Extreme Learning Machine for Motor Imagery Task from EEG Signals , 2016, BICA.

[32]  Abdulkadir Sengur,et al.  Exploring Hermite transformation in brain signal analysis for the detection of epileptic seizure , 2019, IET Science, Measurement & Technology.

[33]  J. Weuve,et al.  2016 Alzheimer's disease facts and figures , 2016 .

[34]  Christos Faloutsos,et al.  Fast Time Sequence Indexing for Arbitrary Lp Norms , 2000, VLDB.

[35]  J. Satheesh Kumar,et al.  Total Variation Based Multi Feature Model for Epilepsy Detection Using Support Vector Machine , 2016 .

[36]  Yanchun Zhang,et al.  Multi-category EEG signal classification developing time-frequency texture features based Fisher Vector encoding method , 2016, Neurocomputing.

[37]  Eamonn J. Keogh,et al.  Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases , 2001, Knowledge and Information Systems.

[38]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.