A computer aided analysis scheme for detecting epileptic seizure from EEG data

Kabir, Enamul; Siuly, Siuly; Cao, Jun; Wang, Hua

doi:10.2991/ijcis.11.1.51

Cited by 39 publications

(17 citation statements)

References 36 publications

(47 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Association rules are used to find the relationship between etiology, syndrome, symptoms and herbal prescription of infectious diseases. In another work, Kabir et al [169] presented the system to detect epileptic seizure from EEG signal using SVM, Naive bayes and logistic regression. Results showed that k-mean clustering can handle EEG data efficiently in order to detect epileptic seizure.…”

Section: Clusteringmentioning

confidence: 99%

Big data analytics for preventive medicine

Razzak

Imran

2019

Neural Comput & Applic

158

View full text Add to dashboard Cite

Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations.

show abstract

Section: Clusteringmentioning

confidence: 99%

Big data analytics for preventive medicine

Razzak

Imran

2019

Neural Comput & Applic

158

View full text Add to dashboard Cite

show abstract

“…Epileptic seizures not only harm the sensory, motor, and functional aspects of the body, but they also affect the consciousness, memory, and cognition of patients [ 5 ]. Therefore, it is of great practical significance to develop an effective detection approach for epileptic seizures [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Detecting Epileptic Seizures in EEG Signals with Complementary Ensemble Empirical Mode Decomposition and Extreme Gradient Boosting

Zhou

2020

Entropy

View full text Add to dashboard Cite

Epilepsy is a common nervous system disease that is characterized by recurrent seizures. An electroencephalogram (EEG) records neural activity, and it is commonly used for the diagnosis of epilepsy. To achieve accurate detection of epileptic seizures, an automatic detection approach of epileptic seizures, integrating complementary ensemble empirical mode decomposition (CEEMD) and extreme gradient boosting (XGBoost), named CEEMD-XGBoost, is proposed. Firstly, the decomposition method, CEEMD, which is capable of effectively reducing the influence of mode mixing and end effects, was utilized to divide raw EEG signals into a set of intrinsic mode functions (IMFs) and residues. Secondly, the multi-domain features were extracted from raw signals and the decomposed components, and they were further selected according to the importance scores of the extracted features. Finally, XGBoost was applied to develop the epileptic seizure detection model. Experiments were conducted on two benchmark epilepsy EEG datasets, named the Bonn dataset and the CHB-MIT (Children’s Hospital Boston and Massachusetts Institute of Technology) dataset, to evaluate the performance of our proposed CEEMD-XGBoost. The extensive experimental results indicated that, compared with some previous EEG classification models, CEEMD-XGBoost can significantly enhance the detection performance of epileptic seizures in terms of sensitivity, specificity, and accuracy.

show abstract

“…Among these, electrical signals recorded using an EEG are believed to provide more information about brain activity and can be collected in a non-invasive manner [7][8][9]. The placement of electrodes for recording the EEG signal is guided either by the benchmark set by the American Academy of Sleep Medicine (AASM) [10,11]; or by the international 10-20 system for the placement of electrodes [10,12].…”

Section: Introductionmentioning

confidence: 99%

Sleep State Classification Using Power Spectral Density and Residual Neural Network with Multichannel EEG Signals

Hasan

Shon

et al. 2020

Applied Sciences

View full text Add to dashboard Cite

This paper proposes a classification framework for automatic sleep stage detection in both male and female human subjects by analyzing the electroencephalogram (EEG) data of polysomnography (PSG) recorded for three regions of the human brain, i.e., the pre-frontal, central, and occipital lobes. Without considering any artifact removal approach, the residual neural network (ResNet) architecture is used to automatically learn the distinctive features of different sleep stages from the power spectral density (PSD) of the raw EEG data. The residual block of the ResNet learns the intrinsic features of different sleep stages from the EEG data while avoiding the vanishing gradient problem. The proposed approach is validated using the sleep dataset of the Dreams database, which comprises of EEG signals for 20 healthy human subjects, 16 female and 4 male. Our experimental results demonstrate the effectiveness of the ResNet based approach in identifying different sleep stages in both female and male subjects compared to state-of-the-art methods with classification accuracies of 87.8% and 83.7%, respectively.

show abstract

A computer aided analysis scheme for detecting epileptic seizure from EEG data

Cited by 39 publications

References 36 publications

Big data analytics for preventive medicine

Big data analytics for preventive medicine

Detecting Epileptic Seizures in EEG Signals with Complementary Ensemble Empirical Mode Decomposition and Extreme Gradient Boosting

Sleep State Classification Using Power Spectral Density and Residual Neural Network with Multichannel EEG Signals

Contact Info

Product

Resources

About