A Rule-Based Classifier to Detect Seizures in EEG Signals
Author:
Funder
Korea Government
Yeungnam University
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s00034-022-02281-3.pdf
Reference32 articles.
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3. M.A. Awal, S. Ouelha, S. Dong et al., A robust high-resolution time–frequency representation based on the local optimization of the short-time fractional Fourier transform. Digit. Signal Process. 70, 125–144 (2017)
4. V. Bajaj, K. Rai, A. Kumar et al., Time–frequency image based features for classification of epileptic seizures from EEG signals. Biomed. Phys. Eng. Express 3, 015012 (2017)
5. A. Bhattacharyya, R.B. Pachori, A. Upadhyay et al., Tunable-q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals. Appl. Sci. 7(4), 385 (2017)
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. One‐dimensional atrous conv‐net based architecture for automatic diagnosis of epilepsy using electroencephalography signals and its brain–computer interface applications;Expert Systems;2023-11-20
2. Method for Automatic Estimation of Instantaneous Frequency and Group Delay in Time–Frequency Distributions with Application in EEG Seizure Signals Analysis;Sensors;2023-05-11
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