scholarly journals Automated Epileptic Seizure Detection in Scalp EEG Based on Spatial-Temporal Complexity

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Xinzhong Zhu ◽  
Huiying Xu ◽  
Jianmin Zhao ◽  
Jie Tian
Keyword(s):  
Real Time ◽  
Epileptic Seizure ◽  
Complexity Analysis ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
Permutation Entropy ◽  
Patient Specific ◽  
Temporal Complexity ◽  
Long Time ◽  
Almost All

Epilepsy is a group of neurological disorders characterized by epileptic seizures, wherein electroencephalogram (EEG) is one of the most common technologies used to diagnose, monitor, and manage patients with epilepsy. A large number of EEGs have been recorded in clinical applications, which leads to visual inspection of huge volumes of EEG not routinely possible. Hence, automated detection of epileptic seizure has become a goal of many researchers for a long time. A novel method is therefore proposed to construct a patient-specific detector based on spatial-temporal complexity analysis, involving two commonly used entropy-based complexity analysis methods, which are permutation entropy (PE) and sample entropy (SE). The performance of spatial-temporal complexity method is evaluated on a shared dataset. Results suggest that the proposed epilepsy detectors achieve promising performance: the average sensitivities of PE and SE in 23 patients are 99% and 96.6%, respectively. Moreover, both methods can accurately recognize almost all the seizure-free EEG. The proposed method not only obtains a high accuracy rate but also meets the real-time requirements for its application on seizure detection, which suggests that the proposed method has the potential of detecting epileptic seizures in real time.

scholarly journals A Novel Permutation Entropy-Based EEG Channel Selection for Improving Epileptic Seizure Prediction

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7972
Author(s):  
Jee S. Ra ◽  
Tianning Li ◽  
Yan Li
Keyword(s):  
Sensitivity And Specificity ◽  
Epileptic Seizure ◽  
Epileptic Seizures ◽  
Optimization Method ◽  
Channel Selection ◽  
Permutation Entropy ◽  
Patient Specific ◽  
Support Vector ◽  
Seizure Prediction ◽  
Epileptic Seizure Prediction

The key research aspects of detecting and predicting epileptic seizures using electroencephalography (EEG) signals are feature extraction and classification. This paper aims to develop a highly effective and accurate algorithm for seizure prediction. Efficient channel selection could be one of the solutions as it can decrease the computational loading significantly. In this research, we present a patient-specific optimization method for EEG channel selection based on permutation entropy (PE) values, employing K nearest neighbors (KNNs) combined with a genetic algorithm (GA) for epileptic seizure prediction. The classifier is the well-known support vector machine (SVM), and the CHB-MIT Scalp EEG Database is used in this research. The classification results from 22 patients using the channels selected to the patient show a high prediction rate (average 92.42%) compared to the SVM testing results with all channels (71.13%). On average, the accuracy, sensitivity, and specificity with selected channels are improved by 10.58%, 23.57%, and 5.56%, respectively. In addition, four patient cases validate over 90% accuracy, sensitivity, and specificity rates with just a few selected channels. The corresponding standard deviations are also smaller than those used by all channels, demonstrating that tailored channels are a robust way to optimize the seizure prediction.

scholarly journals Wearable Detection Systems for Epileptic Seizure: A review

2020 ◽  
Vol 16 (2) ◽  
pp. 1-13
Author(s):  
Alla Fikrat Alwindawi ◽  
Osman Nuri UÇAN ◽  
Ameer Hussein Morad
Keyword(s):  
Epileptic Seizure ◽  
Epileptic Seizures ◽  
Seizure Detection ◽  
Literature Survey ◽  
Research Articles ◽  
Low Power Consumption ◽  
Eeg Signal ◽  
Detection Systems ◽  
Multi Body ◽  
Video Eeg

The seizure epilepsy is risky because it happens randomly and leads to death in some cases. The standard epileptic seizures monitoring system involves video/EEG (electro-encephalography), which bothers the patient, as EEG electrodes are attached to the patient’s head. Seriously, helping or alerting the patient before the seizure is one of the issue that attracts the researchers and designers attention. So that there are spectrums of portable seizure detection systems available in markets which are based on non-EEG signal. The aim of this article is to provide a literature survey for the latest articles that cover many issues in the field of designing portable real-time seizure detection that includes the use of multiple body signals, new algorithm methods, and detection devices that are commercially available. As a result, the reviewing process shows that there are many research articles that have covered wearable seizure detection systems that based on body signals. The more effective monitoring and detection seizure system is the system that uses multi-body signals, is highly comfortable and has low power consumption.

Convolutional neural networks for real-time epileptic seizure detection

2016 ◽  
Vol 6 (3) ◽  
pp. 264-269 ◽  
Author(s):  
Felix Achilles ◽  
Federico Tombari ◽  
Vasileios Belagiannis ◽  
Anna Mira Loesch ◽  
Soheyl Noachtar ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Epileptic Seizure ◽  
Seizure Detection ◽  
Epileptic Seizure Detection
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