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

Topic: Risk, prevention and care in population studies

Alzheimer's disease (AD) accounts for 60%-70% of all dementia cases, and clinical diagnosis at its early stage is extremely difficult. As several new drugs aiming to modify disease progression or alleviate symptoms are being developed, to assess their efficacy, novel robust biomarkers of brain function are urgently required. This paper aims to explore a routine to gain such biomarkers using the quantitative analysis of electroencephalography (QEEG). This paper proposes a supervised classification framework that uses EEG signals to classify healthy controls (HC) and AD participants. The framework consists of data augmentation, feature extraction, K-nearest neighbor (KNN) classification, quantitative evaluation, and topographic visualization. Considering the human brain either as a stationary or a dynamical system, both the frequency-based and time-frequency-based features were tested in 40 participants. The results show that: 1) the proposed method can achieve up to a 99% classification accuracy on short (4s) eyes open EEG epochs, with the KNN algorithm that has best performance when compared with alternative machine learning approaches; 2) the features extracted using the wavelet transform produced better classification performance in comparison to the features based on FFT; and 3) in the spatial domain, the temporal and parietal areas offer the best distinction between healthy controls and AD. The proposed framework can effectively classify HC and AD participants with high accuracy, meanwhile offering identification and the localization of significant QEEG features. These important findings and the proposed classification framework could be used for the development of a biomarker for the diagnosis and monitoring of disease progression in AD.

Key findings

• The results show that: 1) the proposed method can achieve up to a 99% classification accuracy on short (4s) eyes open EEG epochs, with the KNN algorithm that has best performance when compared with alternative machine learning approaches; 2) the features extracted using the wavelet transform produced better classification performance in comparison to the features based on FFT; and 3) in the spatial domain, the temporal and parietal areas offer the best distinction between healthy controls and AD.
• The proposed framework can effectively classify HC and AD participants with high accuracy, meanwhile offering identification and the localization of significant QEEG features.

Why it matters

This study used EEG signals and machine learning to tell apart people with Alzheimer's disease from healthy controls, reaching up to 99% accuracy on short eyes-open recordings. It is about classifying who has the disease, not about treating, curing or preventing it.

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Study details

• Participants40
• Evidence strengthLow

Read the original

• DOI: 10.1109/tnsre.2019.2909100
• PubMed: 30951473
• Original source →
• Free full text: CERES (Cranfield University) →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{durongbhan2019-30951473,
  author  = {Durongbhan, P. and Zhao, Y. and Chen, L. and Zis, P. and De Marco, M. and Unwin, Z. C. and Venneri, A. and He, X. and others},
  title   = {{A Dementia Classification Framework Using Frequency and Time-Frequency Features Based on EEG Signals}},
  journal = {IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society},
  year    = {2019},
  doi     = {10.1109/tnsre.2019.2909100},
  pmid    = {30951473},
  url     = {https://doi.org/10.1109/tnsre.2019.2909100}
}

Studies this one cites

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

• Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria The Lancet Neurology · 2014-05-18 · usefulness score 66/100 · Getting a diagnosis
• Research criteria for the diagnosis of Alzheimer's disease: revising the NINCDS-ADRDA criteria The Lancet. Neurology · 2007-08-01 · usefulness score 63/100 · 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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