Detection of epilepsy with Electroencephalogram using rule-based classifiers

Wang, Guanjin; Deng, Zhaohong; Choi, Kup‐Sze

doi:10.1016/j.neucom.2016.09.080

Cited by 49 publications

(19 citation statements)

References 33 publications

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“…10 and 11. It is confirmed from both the figures that alpha band (8-13 Hz) is associated during visual alertness and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) band is active during motor execution tasks.…”

Section: C3 Pre-processing and Filteringsupporting

confidence: 58%

“…The acquired EEGs from pre-frontal/frontal, parietal and motor cortex regions are pre-processed using band pass filters (BPFs) of suitable frequency bands. VA being more prominent in alpha band (~8-13 Hz) [41] and MP/ME being relatively more active in mu-(8-13 Hz) [42] and beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) [43] bands, we used BPFs of required pass bands. More review on EEG channel selection and frequency band selection are provided in [44], [45].…”

Section: System Design and Integrationmentioning

confidence: 99%

“…In recent times, an increasing interest to classify brain signals is noticed in research community [26], [27]. For example, Wang et al selected random forest algorithm for epilepsy detection for its superior performance over its three competitors, including decision tree and support vector machine (SVM) based realizations of both decision tree and random forest [28]. Herman et al [29] examined the scope of IT2FS induced classifier in motor imagery related EEG classification task for both off-line and online test cases.…”

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confidence: 99%

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EEG Analysis for Cognitive Failure Detection in Driving Using Type-2 Fuzzy Classifiers

Saha

Konar

Nagar

2017

IEEE Trans. Emerg. Top. Comput. Intell.

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Section: C3 Pre-processing and Filteringsupporting

confidence: 58%

Section: System Design and Integrationmentioning

confidence: 99%

mentioning

confidence: 99%

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EEG Analysis for Cognitive Failure Detection in Driving Using Type-2 Fuzzy Classifiers

Saha

Konar

Nagar

2017

IEEE Trans. Emerg. Top. Comput. Intell.

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“…The epileptic seizure-free EEG signals in datasets C and D were also produced, accordingly. Dataset E describes the epileptic seizure signals, which were collected by placing the electrodes in the epileptogenic zone, as shown in Table 1 [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][25][26][27][28][29][30][31][33][34][35][36][37][38][39][40][41][42][43]. The sample segment of each dataset is shown in Figure 1.…”

Section: Eeg Data Materialsmentioning

confidence: 99%

“…Decision Tree (DT) has been verified to have a high interpretability, and the rules, obtained by splitting the training dataset into subsets by testing attributes, are also readily compressible [39].…”

mentioning

confidence: 99%

Cepstrum Coefficient Analysis from Low-Frequency to High-Frequency Applied to Automatic Epileptic Seizure Detection with Bio-Electrical Signals

Ren

Chai

et al. 2018

Applied Sciences

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This study analyzes bioelectrical signals to achieve automatic epileptic seizure detection. Electroencephalographic (EEG) signals were recorded with electrodes on healthy, epileptic seizure-free, and epileptic seizure patients. The challenges in this field are generally regarded to be the impacts of non-stationarity and nonlinearity in EEG signals. To address these challenges, this study attempts to recognize different brain statuses. The idea originated from a novel hypothesis that considers EEG signals as convolution signals and regards itself as the generation mechanism of EEG signals, to some extent. Based on this hypothesis, the nonlinear problem can be viewed as a deconvolution procedure. As such, the method can be simplified into three parts: eliminating non-stationary is used to catch high-frequency to low-frequency signals, which is followed by a local mean decomposition (LMD) algorithm; these signals are deconvoluted to form ultra-high-dimensional feature sets, which is completely terminated by the mel-frequency cepstrum coefficients (MFCC) algorithm; and several classifiers are combined to achieve highly accurate recognition results and to verify the superiority and reasonableness of this method. The publicly available EEG database from the University of Bonn, Germany is employed to demonstrate the effectiveness and outstanding performance of this method. According to the results, the method has the ability to attain a higher average classification accuracy than other methods in all of the four following cases: healthy (datasets A and B) versus epileptic seizure (dataset E), epileptic seizure-free (datasets C and D) versus epileptic seizure (dataset E), healthy (datasets A and B) versus epileptic seizure-free (datasets C and D) versus epileptic seizure (dataset E), and healthy (dataset A) versus healthy (dataset B) versus epileptic seizure-free (dataset C) versus epileptic seizure-free (dataset D) versus epileptic seizure (dataset E).

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