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Detection of Alzheimer's Dementia by Using Signal Decomposition and Machine Learning Methods.

Ozlem Karabiber Cura1, Aydin Akan2, Gulce Cosku Yilmaz3

  • 1Department of Biomedical Engineering, Izmir Katip Celebi University, Cigli, 35620 Izmir, Turkey.

International Journal of Neural Systems
|August 10, 2022
PubMed
Summary

This study uses electroencephalography (EEG) signal analysis to detect Alzheimer's dementia (AD). Advanced signal processing methods like EMD, EEMD, and DWT show high accuracy in differentiating AD patients from control subjects.

Keywords:
Dementiadiscrete wavelet transform (DWT)electroencephalography (EEG)empirical mode decomposition (EMD)ensemble EMD (EEMD)machine learning

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Area of Science:

  • Neurology
  • Biomedical Engineering
  • Signal Processing

Background:

  • Dementia, particularly Alzheimer's disease (AD), is a prevalent neurological disorder significantly impacting cognitive function and quality of life.
  • Early and accurate detection of AD is crucial for timely intervention and management.
  • Electroencephalography (EEG) signals offer a potential non-invasive method for monitoring neurological conditions like AD.

Purpose of the Study:

  • To evaluate the efficacy of advanced signal processing techniques for detecting and monitoring Alzheimer's dementia (AD) using EEG signals.
  • To compare the performance of Empirical Mode Decomposition (EMD), Ensemble EMD (EEMD), and Discrete Wavelet Transform (DWT) in classifying EEG segments from AD patients and control subjects.
  • To identify optimal signal decomposition and feature extraction methods for improved AD classification.

Main Methods:

  • EEG signals were analyzed using signal decomposition methods: Empirical Mode Decomposition (EMD), Ensemble EMD (EEMD), and Discrete Wavelet Transform (DWT).
  • Intrinsic Mode Functions (IMFs) were extracted using EMD and EEMD, with significant differentiating IMFs selected based on established criteria.
  • Time-domain and spectral-domain features were calculated from selected IMFs and DWT coefficients for both 1-minute and 5-second EEG segments.

Main Results:

  • All proposed signal decomposition methods demonstrated significant classification performance for 1-minute EEG segments.
  • The EMD approach achieved a classification accuracy of 91.8%, and EEMD achieved 94.1% from the temporal/right brain cluster.
  • The DWT approach yielded the highest classification accuracy of 95.2% from the temporal/left brain cluster for 1-minute segments.

Conclusions:

  • Advanced signal processing techniques, including EMD, EEMD, and DWT, are effective for classifying EEG signals in Alzheimer's dementia detection.
  • The choice of decomposition method and brain region cluster can influence classification accuracy.
  • These findings support the potential of EEG-based signal analysis for the non-invasive diagnosis and follow-up of AD.