Analysis of Epileptic EEG Signals with Simple Random Sampling J48 Algorithm

Wang, Shuaifang; Zhu, Guohun; Li, Yan; Wen, Peng; Song, Bo

doi:10.7763/ijbbb.2014.v4.314

Cited by 4 publications

(7 citation statements)

References 23 publications

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“…The highest overall classification accuracies are highlighted in bold font. The best performance was obtained from the OA technique combined with the AR estimation method as well as SVM classifier, compared with the results obtained by the authors of [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. Even though the extracted features by using the OA combined with the mentioned spectral methods were scored the second highest accuracy with a 99.9%, it is considered acceptable in the research of epileptic seizures detection.…”

Section: Resultsmentioning

confidence: 73%

Section: Resultsmentioning

confidence: 80%

“…A versus E 100 D versus E 93.0 A, B, C, D versus E 95.4 Wang et al [29] • simple random sampling + J48…”

Section: Acknowledgmentmentioning

confidence: 99%

“…In the same year (2014), a combination of simple random sampling technique with J48 algorithm was implemented and achieved 100, 95.6, and 97.3% classification accuracy for three group: A versus E, B versus E, and C versus E, respectively, by Wang et al . [29].…”

Section: Introductionmentioning

confidence: 99%

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Epileptic EEG signal classification using optimum allocation based power spectral density estimation

Ghayab

Siuly

et al. 2018

IET signal process.

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This study proposes a novel approach blending optimum allocation (OA) technique and spectral density estimation to analyse and classify epileptic electroencephalogram (EEG) signals. This study employs the OA to determine representative sample points from the original EEG data and then applies periodogram (PD), autoregressive (AR), and the mixture of PD and AR to extract the discriminative features from each OA sample group. The obtained feature sets are evaluated by three popular machine learning methods: support vector machine (SVM), quadratic discriminant analysis (QDA), and k-nearest neighbour (k-NN). Several output coding approaches of the SVM classifier are tested for selecting the best feature sets. This scheme was implemented on a benchmark epileptic EEG database for evaluation and also compared with existing methods. The experimental results show that the OA_AR feature set yields better performances by the SVM with an overall accuracy of 100%, and outperforms the state-of-the-art works with a 14.1% improvement. Thus, the findings of this study prove that the proposed OA-based AR scheme has significant potential to extract features from EEG signals. The proposed method will assist experts to automatically analyse a large volume of EEG data and benefit epilepsy research.

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Section: Resultsmentioning

confidence: 73%

Section: Resultsmentioning

confidence: 80%

“…A versus E 100 D versus E 93.0 A, B, C, D versus E 95.4 Wang et al [29] • simple random sampling + J48…”

Section: Acknowledgmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Epileptic EEG signal classification using optimum allocation based power spectral density estimation

Ghayab

Siuly

et al. 2018

IET signal process.

Self Cite

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“…Here, we built an ensemble classifier called LibMutil that uses eight common classifiers, namely AdaBoost.M131, Bagging32, Naïve Bayes33, Logistic34, Random Forest35, Random Tree36, J4837, and KNN 38. The AdaBoost.M1, Bagging, Random Tree, and Random Forest classifiers are themselves ensemble learners; therefore, we used these classifiers as base learners to refine the performance of LibMutil, which in turn helped to improve the sensitivity of tRNAscan‐SE for pseudo‐tRNAs.…”

Section: Methodsmentioning

confidence: 99%

Improving tRNAscan‐SE Annotation Results via Ensemble Classifiers

Zou

Guo

et al. 2015

Molecular Informatics

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tRNAScan-SE is a tRNA detection program that is widely used for tRNA annotation; however, the false positive rate of tRNAScan-SE is unacceptable for large sequences. Here, we used a machine learning method to try to improve the tRNAScan-SE results. A new predictor, tRNA-Predict, was designed. We obtained real and pseudo-tRNA sequences as training data sets using tRNAScan-SE and constructed three different tRNA feature sets. We then set up an ensemble classifier, LibMutil, to predict tRNAs from the training data. The positive data set of 623 tRNA sequences was obtained from tRNAdb 2009 and the negative data set was the false positive tRNAs predicted by tRNAscan-SE. Our in silico experiments revealed a prediction accuracy rate of 95.1 % for tRNA-Predict using 10-fold cross-validation. tRNA-Predict was developed to distinguish functional tRNAs from pseudo-tRNAs rather than to predict tRNAs from a genome-wide scan. However, tRNA-Predict can work with the output of tRNAscan-SE, which is a genome-wide scanning method, to improve the tRNAscan-SE annotation results. The tRNA-Predict web server is accessible at http://datamining.xmu.edu.cn/∼gjs/tRNA-Predict.

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An Extended K Nearest Neighbors-Based Classifier for Epilepsy Diagnosis

Wang

et al. 2021

IEEE Access

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In the diagnosis of epileptic seizures, classification is an important step that directly affects the results. Visual inspection of Electroencephalogram (EEG) is a relatively common analytic method of epilepsy, but it is costly, time-consuming and relies on the experiences of the doctor. Therefore, the development of an efficient and accurate epileptic seizure automatic diagnosis system suitable for clinical diagnosis has become an urgent task. In order to better solve the problem of early diagnosis of epileptic and bring timely treatment to patients, the comprehensive representation of k nearest neighbors for multi-distance decision making (CRMKNN) is proposed in this research. In the proposed scheme, Euclidean distance and Hassanat distance are firstly used to select neighbors. Subsequently, the similarity distance is obtained through the linear representation of the nearest neighbors, and calculate the distribution of nearest neighbors in the category to get the discrete distance. Finally, the distance based on the comprehensive representation of the category is used to determine the category of the query EEG signal. In order to verify the method, we used the EEG signals from Bonn university public database and conducted experiments on six kinds of EEG combinations. Experimental results showed that our method could automatically detect seizure in all situations with an accuracy of not less than 99.50%. At the same time, compared with the classification results of existing methods, this method is more effective.

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Analysis of Epileptic EEG Signals with Simple Random Sampling J48 Algorithm

Cited by 4 publications

References 23 publications

Epileptic EEG signal classification using optimum allocation based power spectral density estimation

Epileptic EEG signal classification using optimum allocation based power spectral density estimation

Improving tRNAscan‐SE Annotation Results via Ensemble Classifiers

An Extended K Nearest Neighbors-Based Classifier for Epilepsy Diagnosis

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