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Purpose <p>This paper investigates the well-known Empirical Wavelet Transform (EWT) and the recently introduced Empirical Fourier Decomposition (EFD) for the early diagnosis of Alzheimer's disease (AD). Both synthetic signals and real EEG data are decomposed and reconstructed, particularly under noisy conditions.</p> Methods <p>EWT and EFD were applied to decompose non-stationary EEG signals into five sub-bands (Delta, Theta, Alpha, Beta, and Gamma). From each sub-band, eight features were extracted and used to classify subjects into AD and Mild Cognitive Impairment (MCI) groups. Among the five classifiers tested, Random Forest (RF) yielded the best performance for both EWT and EFD. In addition to conventional evaluation metrics, Dynamic Time Warping (DTW) and the Kolmogorov–Smirnov (KS) statistic were used for algorithm assessment.</p> Results <p>The results show that EFD outperforms EWT and achieves competitive performance compared to state-of-the-art approaches.</p> Conclusion <p>EFD is a novel decomposition method that demonstrates robust performance on both synthetic and real EEG signals, supporting its potential use in the early diagnosis of Alzheimer's disease.</p>

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EFD in Comparison with EWT for Synthetic and EEG Signal Decomposition and Classification of Alzheimer’s Disease and Mild Cognitive Impairment

  • 期刊论文

  • 发表日期：

    2025年11月16日

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  • Saba Haji Molla Rabie,
  • Sedigheh Ghofrani,
  • Hamideh Barghamadi,
  • Mahdi Eslami

摘要

Purpose

This paper investigates the well-known Empirical Wavelet Transform (EWT) and the recently introduced Empirical Fourier Decomposition (EFD) for the early diagnosis of Alzheimer's disease (AD). Both synthetic signals and real EEG data are decomposed and reconstructed, particularly under noisy conditions.

Methods

EWT and EFD were applied to decompose non-stationary EEG signals into five sub-bands (Delta, Theta, Alpha, Beta, and Gamma). From each sub-band, eight features were extracted and used to classify subjects into AD and Mild Cognitive Impairment (MCI) groups. Among the five classifiers tested, Random Forest (RF) yielded the best performance for both EWT and EFD. In addition to conventional evaluation metrics, Dynamic Time Warping (DTW) and the Kolmogorov–Smirnov (KS) statistic were used for algorithm assessment.

Results

The results show that EFD outperforms EWT and achieves competitive performance compared to state-of-the-art approaches.

Conclusion

EFD is a novel decomposition method that demonstrates robust performance on both synthetic and real EEG signals, supporting its potential use in the early diagnosis of Alzheimer's disease.