Online Seizure Prediction Using an Adaptive Learning Approach

Wang, Shouyi; Chaovalitwongse, Wanpracha Art; Wong, S.S.

doi:10.1109/tkde.2013.151

Cited by 67 publications

(53 citation statements)

References 37 publications

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“…Wang et al presented a wavelet-based online adaptive seizure prediction system [56]. This system adopts Lyapunov exponent, correlation dimension, Hurst exponent, and entropy features extracted from the wavelet transform of EEG recordings.…”

Section: Wavelet-domain Seizure Predictionmentioning

confidence: 99%

EEG seizure detection and prediction algorithms: a survey

Alshebeili

Alshawi

Ahmad

et al. 2014

EURASIP J. Adv. Signal Process.

231

115

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Epilepsy patients experience challenges in daily life due to precautions they have to take in order to cope with this condition. When a seizure occurs, it might cause injuries or endanger the life of the patients or others, especially when they are using heavy machinery, e.g., deriving cars. Studies of epilepsy often rely on electroencephalogram (EEG) signals in order to analyze the behavior of the brain during seizures. Locating the seizure period in EEG recordings manually is difficult and time consuming; one often needs to skim through tens or even hundreds of hours of EEG recordings. Therefore, automatic detection of such an activity is of great importance. Another potential usage of EEG signal analysis is in the prediction of epileptic activities before they occur, as this will enable the patients (and caregivers) to take appropriate precautions. In this paper, we first present an overview of seizure detection and prediction problem and provide insights on the challenges in this area. Second, we cover some of the state-of-the-art seizure detection and prediction algorithms and provide comparison between these algorithms. Finally, we conclude with future research directions and open problems in this topic.

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Section: Wavelet-domain Seizure Predictionmentioning

confidence: 99%

EEG seizure detection and prediction algorithms: a survey

Alshebeili

Alshawi

Ahmad

et al. 2014

EURASIP J. Adv. Signal Process.

231

115

View full text Add to dashboard Cite

show abstract

“…Machine learning techniques could be grouped into three classes: supervised learning, unsupervised learning, and reinforcement learning [26]. Adaptive learning is closely related to reinforcement learning in which the system reinforce new knowledge based on the online feedback or try and error, not by pre-defined training process.…”

Section: E Adaptive Learningmentioning

confidence: 99%

“…Based on author based knowledge, there is limited prior study that investigated adaptive learning for seizure detection. In related topic, Wang et al [26], and followed by us [27], proposed an adaptive learning approach for seizure prediction. This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

E EG - Based Epileptic Seizures Detection with Adaptive Learning Capability

Ibrahim¹,

Majzoub²

2017

ijeei

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Epilepsy is considered one of the most common neurological disorders. Epileptic seizures can be a major life disability that might result in loss of consciousness, and/or injury to oneself or others. This research work aims to develop an epileptic seizure detection method using electroencephalography (EEG) signal analysis. We combine discrete wavelet transform (DWT), Shannon entropy, and statistical feature (standard deviation) to extract distinctive features of a given EEG signal. Knearest neighbors (KNN) automatically classifies the EEG signal by comparing the extracted features with the features of the normal and seizure baseline. Adaptive learning is used to continuously update the two baselines based the user feedback, if needed, to improve its performance over the time. Our proposed method achieved an overall 94.5 % sensitivity tested with up to 570 hours of continuous EEG recording from ten patients with total of 55 seizure events taken from CHB-MIT database. With its simplicity and fast processing time, the proposed method is suitable to be implemented in embedded device which has limited processing resource.

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“…Designing an automated system to detect epilepsy on time would be helpful and time-saving for the neurologist. Lately, numerous mechanized frameworks have been proposed for the forecast and recognition of epilepsy with distinctive time and frequency domain features, non-linear features with pattern classifiers (Gotman and Deng 1991;Acharya et al 2012a, b, c;Bajaj and Pachori 2013;Wang et al 2013;Venkataraman et al 2014;Samiee et al 2015;Faust et al 2015;Du et al 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Any EEG-based quantitative study towards epileptic seizure detection requires the appropriate application of filtering, segmentation, features selection and pattern classifier techniques. Although several attempts have been made by make use of entropy as a feature pattern to detect epileptic seizures from EEG recordings (Aydin et al 2009;Pravin et al 2010;Guo et al 2010;Wang et al 2013), there are still unsolved issues related to computational complexity and overall performance. The appropriate selection of feature and pattern classifier by considering its relative performance will confirm its suitability for the real time seizure detection.…”

Section: Introductionmentioning

confidence: 99%

Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier

Raghu¹,

Kumar

2016

Cogn Neurodyn

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Electroencephalogram shortly termed as EEG is considered as the fundamental segment for the assessment of the neural activities in the brain. In cognitive neuroscience domain, EEG-based assessment method is found to be superior due to its non-invasive ability to detect deep brain structure while exhibiting superior spatial resolutions. Especially for studying the neurodynamic behavior of epileptic seizures, EEG recordings reflect the neuronal activity of the brain and thus provide required clinical diagnostic information for the neurologist. This specific proposed study makes use of wavelet packet based log and norm entropies with a recurrent Elman neural network (REN) for the automated detection of epileptic seizures. Three conditions, normal, pre-ictal and epileptic EEG recordings were considered for the proposed study. An adaptive Weiner filter was initially applied to remove the power line noise of 50 Hz from raw EEG recordings. Raw EEGs were segmented into 1 s patterns to ensure stationarity of the signal. Then wavelet packet using Haar wavelet with a five level decomposition was introduced and two entropies, log and norm were estimated and were applied to REN classifier to perform binary classification. The nonlinear Wilcoxon statistical test was applied to observe the variation in the features under these conditions. The effect of log energy entropy (without wavelets) was also studied. It was found from the simulation results that the wavelet packet log entropy with REN classifier yielded a classification accuracy of 99.70 % for normal-pre-ictal, 99.70 % for normal-epileptic and 99.85 % for pre-ictalepileptic.

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Online Seizure Prediction Using an Adaptive Learning Approach

Cited by 67 publications

References 37 publications

EEG seizure detection and prediction algorithms: a survey

EEG seizure detection and prediction algorithms: a survey

E EG - Based Epileptic Seizures Detection with Adaptive Learning Capability

Classification of epileptic seizures using wavelet packet log energy and norm entropies with recurrent Elman neural network classifier

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