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Discrete Fourier Transform01:15

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The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
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The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
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Related Experiment Video

Updated: Feb 10, 2026

Meta-analysis of Voxel-Based Neuroimaging Studies using Seed-based d Mapping with Permutation of Subject Images SDM-PSI
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[Epileptic electroencephalogram recognition based on discrete S transform and permutation entropy].

Jianzhao Zhang1, Wei Jiang2, Hui Yuan1

  • 1School of Information Science and Engineering, Shandong University, Jinan 250100, P.R.China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi
|May 16, 2018
PubMed
Summary

This study introduces a novel method for automatic epilepsy diagnosis using Electroencephalogram (EEG) signals. Combining S transform and permutation entropy significantly improves recognition accuracy for epileptic EEG classification.

Keywords:
S transformelectroencephalogramepilepsypermutation entropy

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Area of Science:

  • Neuroscience
  • Signal Processing
  • Machine Learning

Context:

  • Electroencephalogram (EEG) analysis is crucial for epilepsy diagnosis.
  • Current methods using single EEG features have limitations in recognition accuracy.
  • Automating epileptic EEG classification is vital for timely clinical intervention.

Purpose:

  • To develop an automated method for discriminating epileptic EEG signals.
  • To overcome limitations of single-feature analysis and wavelet basis function selection.
  • To enhance the accuracy of epilepsy diagnosis through a combined feature approach.

Summary:

  • A novel method combines S transform and permutation entropy for epileptic EEG signal analysis.
  • The approach calculates rhythm fluctuation indices and integrates permutation entropy into a feature vector.
  • A Real AdaBoost classifier is employed for multi-period discrimination of epileptic EEG signals.

Impact:

  • Achieved an average recognition accuracy of 98.13% in classifying normal, interictal, and ictal epileptic EEG signals.
  • Demonstrated superior performance compared to single time-frequency or nonlinear feature extraction methods.
  • The proposed method shows significant potential for improving the clinical diagnosis of epilepsy.