Epileptic seizure detection in long-term EEG records using sparse rational decomposition and local Gabor binary patterns feature extraction

Samiee, Kaveh; Kovcs, Pter; Gabbouj, Moncef

doi:10.1016/j.knosys.2016.11.023

Cited by 92 publications

(40 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is worth mentioning that the cited research works have used a variety of datasets and therefore it would be difficult to do and exact comparison of results. [4] CHB-MIT 95.7 Samiee (2017) [9] CHB-MIT Sensitivity (91.13) Guo et al (2011) [17] A, E 95. 2 Mihandoost et al (2012) [12] A, D, E 98.87 Fu et al (2014) [2] A, E 99.13 Sharma and Pachori (2015) [7] C, D, E 98.67…”

Section: Resultsmentioning

confidence: 99%

“…Different researchers addressed the problem of automatically detecting epilepsy using different approaches [2]- [9]. A recent survey on the topic can be cited in [10].…”

Section: Literature Surveymentioning

confidence: 99%

“…A recent survey on the topic can be cited in [10]. The proposed approaches used different types of features such as wavelet transform [11], [12]; Fourier transform [13]; or other feature extraction methods [2]- [9] . Elmahdy et al proposed the use of two types of features; viz.…”

Section: Literature Surveymentioning

confidence: 99%

“…The authors reported that the SVM classifier obtained better results compared to Artificial Neural Networks, with a 93.75% accuracy. A recent study was proposed by Samiee et al They used a technique that utilizes rational decomposition of EEG signals and 1D LGBP for feature extraction [9]. They extracted features by decomposing the EEG signal into 8 sparse rational components.…”

Section: Literature Surveymentioning

confidence: 99%

See 3 more Smart Citations

Automatic Detection of Epilepsy Using EEG Energy and Frequency Bands

Bellegdi

Arafat

2017

ijamec

View full text Add to dashboard Cite

This paper demonstrates the effectiveness of information fusion at the feature vectors level for automatic detection of epilepsy. Experiments used features ranging from separate EEG frequency band waves to combinations of band waves, in addition to signal energy. We used three classifiers with the feature vectors: TreeBoost, Random Forests, and support vector machines. We carried out experiments using a real life EEG signals data set that is available from the University of Bonn Hospital in Germany. This paper shows the effect of combining together signal energy with different EEG frequency band waves in order to classify epilepsy, and that this combination has computed 97.5% accuracy over using feature vectors with fewer band wave transformations (84-95.5% accuracy), using the TreeBoost algorithm and 10 folds cross validation. This combination computed 99% specificity and 95.5% sensitivity. Furthermore, the paper demonstrates and analyses the effectiveness of using ensemble based tree learning.

show abstract

Section: Resultsmentioning

confidence: 99%

“…Different researchers addressed the problem of automatically detecting epilepsy using different approaches [2]- [9]. A recent survey on the topic can be cited in [10].…”

Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

See 2 more Smart Citations

Automatic Detection of Epilepsy Using EEG Energy and Frequency Bands

Bellegdi

Arafat

2017

ijamec

View full text Add to dashboard Cite

show abstract

“…The Gabor filters find major application in the pattern analysis. The Gabor filters are used commonly because it has a better quality of extraction of the text patterns from the large complex images [28]. The Gabor filters find the text of the both 2D and the 3D images and hence can be used in the lecture video retrieval system.…”

Section: C) Local Gabor Pattern (Lgp)mentioning

confidence: 99%

Semantic Enriched Lecture Video Retrieval System Using Feature Mixture and Hybrid Classification

Daga¹,

Thakare

2017

AIVP

View full text Add to dashboard Cite

The advancement in the web technologies has increased the lecture video contents tremendously. The lecture video retrieval for the e-learning process is a challenging task since the videos are unstructured and have a large size. Since many video lectures have less information, the video retrieval system needs to be built with the enhanced features to improve the efficiency of the retrieval process. In this paper, a semantic enriched lecture video retrieval system has been proposed. The key frames from the video are extracted through the pre-processing. The proposed model uses the feature mixture database with the more relevant features such as text, semantic word, and the Local Gabor Pattern (LGP) vectors. The video retrieval from the feature mixture database is done by using the hybrid K-Nearest Neighbour Naive Bayes (KNB) classifier. This classifier uses the techniques of both the Naive Bayes (NB) classifier and the K-Nearest Neighbour (K-NN) classifier. The performance metrics such as precision, recall and the f-measure analyze the efficiency of the proposed model. Simulation is done by giving the text query and the video query to the video database. The simulation results show that the proposed model has better precision and recall value of 1.0 and 0.7500 respectively. The f-measure of the proposed model has a better value of 0.8519 than the existing K-NN system.

show abstract