A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia

Topic: Risk, prevention and care in population studies

Electroencephalographic (EEG) recordings generate an electrical map of the human brain that are useful for clinical inspection of patients and in biomedical smart Internet-of-Things (IoT) and Brain-Computer Interface (BCI) applications. From a signal processing perspective, EEGs yield a nonlinear and nonstationary, multivariate representation of the underlying neural circuitry interactions. In this paper, a novel multi-modal Machine Learning (ML) based approach is proposed to integrate EEG engineered features for automatic classification of brain states. EEGs are acquired from neurological patients with Mild Cognitive Impairment (MCI) or Alzheimer's disease (AD) and the aim is to discriminate Healthy Control (HC) subjects from patients. Specifically, in order to effectively cope with nonstationarities, 19-channels EEG signals are projected into the time-frequency (TF) domain by means of the Continuous Wavelet Transform (CWT) and a set of appropriate features (denoted as CWT features) are extracted from δ, θ, α 1 , α 2 , β EEG sub-bands. Furthermore, to exploit nonlinear phase-coupling information of EEG signals, higher order statistics (HOS) are extracted from the bispectrum (BiS) representation. BiS generates a second set of features (denoted as BiS features) which are also evaluated in the five EEG sub-bands. The CWT and BiS features are fed into a number of ML classifiers to perform both 2-way (AD vs. HC, AD vs. MCI, MCI vs. HC) and 3-way (AD vs. MCI vs. HC) classifications. As an experimental benchmark, a balanced EEG dataset that includes 63 AD, 63 MCI and 63 HC is analyzed. Comparative results show that when the concatenation of CWT and BiS features (denoted as multi-modal (CWT+BiS) features) is used as input, the Multi-Layer Perceptron (MLP) classifier outperforms all other models, specifically, the Autoencoder (AE), Logistic Regression (LR) and Support Vector Machine (SVM). Consequently, our proposed multi-modal ML scheme can be considered a viable alternative to state-of-the-art computationally intensive deep learning approaches.

Key findings

• Comparative results show that when the concatenation of CWT and BiS features (denoted as multi-modal (CWT+BiS) features) is used as input, the Multi-Layer Perceptron (MLP) classifier outperforms all other models, specifically, the Autoencoder (AE), Logistic Regression (LR) and Support Vector Machine (SVM).

Why it matters

A machine learning approach using EEG features classified healthy controls versus patients with mild cognitive impairment or Alzheimer's disease, with one classifier performing best. This is a diagnostic classification study and does not test any treatment, cure, or prevention.

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Study details

• Evidence strengthLow

Read the original

• DOI: 10.1016/j.neunet.2019.12.006
• PubMed: 31884180
• Original source →

May need a subscription OpenAlex found no free copy of the full text (checked 6 Feb 1020), so the publisher may charge for it. The PubMed abstract is free.

@article{ieracitano2020-31884180,
  author  = {Ieracitano, C. and Mammone, N. and Hussain, A. and Morabito, F. C.},
  title   = {{A novel multi-modal machine learning based approach for automatic classification of EEG recordings in dementia}},
  journal = {Neural networks : the official journal of the International Neural Network Society},
  year    = {2020},
  doi     = {10.1016/j.neunet.2019.12.006},
  pmid    = {31884180},
  url     = {https://doi.org/10.1016/j.neunet.2019.12.006}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

• Mild cognitive impairment: clinical characterization and outcome Archives of neurology · 1999-03-01 · usefulness score 70/100 · How common it is
• Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease Alzheimer's & dementia : the journal of the Alzheimer's Association · 2011-04-21 · usefulness score 70/100 · Getting a diagnosis
• Improving Alzheimer's disease diagnosis with machine learning techniques Clinical EEG and neuroscience · 2011-07-01 · usefulness score 42/100 · Digital health tools

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• Event-related potential markers of subjective cognitive decline and mild cognitive impairment during a sustained visuo-attentive task NeuroImage Clinical · 2025-01-01 · usefulness score 47/100 · Mild cognitive impairment
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