Automatic Diagnosis of Mild Cognitive Impairment from Electroencephalogram Using Joint Wavelet Packet Decomposition and Common Spatial Pattern

Document Type : Original Article (s)

Authors

1 PhD Candidate, Department of Biomedical Engineering, Student Research Committee, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

2 Professor, Department of Biomedical Engineering, Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran

3 Department of Biomedical Engineering, Isfahan University of Medical Sciences, Isfahan, Iran

Abstract

Background: Mild cognitive impairment (MCI) is identified as the initial stage of Alzheimer's disease. This condition presents less severe symptoms compared to Alzheimer's Disease (AD) to the extent that it does not significantly impact daily activities. Due to its subtle symptoms, diagnosing MCI is considerably more challenging than diagnosing Alzheimer's. However, early detection of MCI enhances the chances of treatment and prevention of its progression to Alzheimer's and dementia.
Methods: This study introduced a novel method for diagnosing MCI using an automated signal processing approach for electroencephalogram (EEG) signals. The method employs advanced signal processing techniques, including discrete wavelet transform in preprocessing and wavelet packet decomposition alongside spatial-spectral filters for feature extraction from EEG signals. EEG signals from 29 patients and 32 healthy individuals were utilized in this study.
Findings: The proposed method achieved a classification accuracy of 100% using a random subsampling validation approach. Wavelet packet decomposition effectively isolated frequency sub-bands within the EEG signals, enabling precise extraction. Furthermore, feature extraction using features extracted by the filter bank common spatial pattern (FBCSP) contributed to the increased classification accuracy of the two groups.
Conclusion: This study introduces a novel approach for MCI diagnosis by extracting spatial-spectral features from frequency sub-bands of EEG signals obtained through wavelet packet decomposition. The findings underscore the significance of wavelet packet decomposition in separating frequency sub-bands and applying a common spatial pattern filter on these sub-bands for effective feature extraction in distinguishing healthy individuals from those with MCI.

Highlights

Mohammadali Ganjali: Google Scholar, PubMed

Alireza Mehridehnavi: Google Scholar, PubMed

Vahid Sadeghi: Google Scholar

Keywords

Main Subjects

References

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Journal of Isfahan Medical School
Volume 42, Issue 773
1st Week, September
September and October 2024
Pages 553-559

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History

  • Receive Date: 26 June 2024
  • Revise Date: 01 July 2024
  • Accept Date: 17 September 2024

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