Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy

Ramgopal, Sriram; Thomé‐Souza, Sigride; Jackson, Michele; Kadish, Navah Ester; Fernández, Iván Sánchez; Klehm, Jacquelyn; Bosl, William J.; Reinsberger, Claus; Schachter, Steven C.; Loddenkemper, Tobias

doi:10.1016/j.yebeh.2014.06.023

Cited by 387 publications

(269 citation statements)

References 117 publications

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“…This model is a modified form of Lyapunov exponent [14], to effectively operate on time series data. Other EEG based seizure prediction systems include a validation based model by Yang et al [15], a closed loop warning system proposed by Ramgopal et al [16] and a regularity based seizure prediction model by Chien et al [17]. The major disadvantage of the existing approaches is the tradeoff that had occurred between time and accuracy of predictions.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Customized Seizure Prediction on Streaming EEG Data using Attribute Extraction and Feature Identification Techniques

Ramina¹,

Vanitha²

2017

IJCA

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Epilepsy is considered to be a neurological disorder caused by unoriented signal emissions from brain, leading to seizures. Prior identification of occurrence of seizures is made possible by measuring the signal emissions at certain parts of the brain, known as EEG. Fast detection of preictal signals can alert patients to prevent catastrophe. However, EEG signals are voluminous and have very high velocity rates, making the prediction process complex. This paper presents an effective seizure prediction model, that enhances predictions by identifying frequency based features and performs two level data reduction to enable faster processing. The processed data is then passed to GBT, a boosted ensemble model for prediction. Experiments were conducted with data obtained from American Epilepsy Society. Results indicate good performances in terms of ROC and PR. A comparison with an existing parallel bagging based seizure prediction model indicates improved accuracy levels in the proposed model.

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Section: Related Workmentioning

confidence: 99%

Real-Time Customized Seizure Prediction on Streaming EEG Data using Attribute Extraction and Feature Identification Techniques

Ramina¹,

Vanitha²

2017

IJCA

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“…However, EEG interpretation becomes tedious due to variability in amplitude, phase, frequency, and non-periodic features. Neurologists analyze the recordings by the review of large datasets, being a time-consuming, stressful and subjective diagnostic process (Arunkumar et al, 2012;Fergus et al, 2015;Peker et al, 2016;Ramgopal et al, 2014;Wang et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Several techniques were explored in order to improve the performance of Wavelet multiresolution analysis and dyadic scalogram for detection of epileptiform paroxysms in electroencephalographic signals automated systems (Arunkumar et al, 2012;Fergus et al, 2015;Gotman and Gloor, 1976;Hunyadi et al, 2011;Olejarczyk et al, 2009;Peker et al, 2016;Petersen et al, 2013;Ramgopal et al, 2014;Wang et al, 2014). In the last decades, a very powerful tool called wavelet transform was applied to solve this problem (Adeli et al, 2007;Ayoubian et al, 2013;Haydari et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Wavelet multiresolution analysis and dyadic scalogram for detection of epileptiform paroxysms in electroencephalographic signals

Malaver

2017

Res. Biomed. Eng.

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Introduction: Early detection of epilepsy by the review of large electroencephalographic (EEG) recordings is very stressful, time-consuming, and subjective for neurologists. Several automatic seizure detection systems have been proposed in the literature to solve this problem. Methods: This study proposes two complementary wavelet-based approaches for detecting epileptiform paroxysms in EEG signals. First methodology applied the wavelet multiresolution analysis (MRA) to filter non-epileptiform activity in long-term EEG. Second methodology used the wavelet dyadic scalogram to analyze which scales were related to the epileptiform paroxysms. For tests, 65 wavelet functions were selected between daubechies, biorthogonal, symlets, reverse biorthogonal and coiflet wavelet families in order to evaluate their performance. Results: For MRA, it was noted a better performance by using the db4 function, by reaching 48.30% of energy with 8 wavelet coefficients, 0.717658 of correlation and 36.799 of root mean square error (RMSE). For wavelet dyadic scalograms, were chosen bior3.9 and rbio1.5 functions, by reaching 77.98% of sensitivity, 94.08% of specificity, 87.87% of efficiency and 0.9613 of area under the curve (AUC value) by using bior3.9. Conclusion: The presented approaches are highly complementary for a whole automatic seizure detection system by using the MRA as pre-processing stage to filter non-epileptiform activity, and wavelet dyadic scalogram for extracting desired features from filtered EEG signals.

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“…Scalp EEG, both wet and dry electrode [4], have been used for seizure detection, sleep monitoring and etc., [1], [2], [3]. However, long-term monitoring becomes their limitation for many practical applications since it requires privacy (invisible to the people) and wearing comfort.…”

Section: Introductionmentioning

confidence: 99%

A new soft material based in-the-ear EEG recording technique

Dong

Matthews

Guo

2016

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Abstract-Long-term electroecephalogram (EEG) recording is important for seizure detection, sleep monitoring and etc. In-the-ear EEG device makes such recording robust to noise and privacy protected (invisible to other people). However, the state-of-art techniques suffers from various drawbacks such as customization for specific users, manufacturing difficulties and short life cycle. In this paper, we proposed a new soft material based in-the-ear EEG recording technique to address all the issues. Moreover, our techniques allows mass production with low cost.

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Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy

Cited by 387 publications

References 117 publications

Real-Time Customized Seizure Prediction on Streaming EEG Data using Attribute Extraction and Feature Identification Techniques

Real-Time Customized Seizure Prediction on Streaming EEG Data using Attribute Extraction and Feature Identification Techniques

Wavelet multiresolution analysis and dyadic scalogram for detection of epileptiform paroxysms in electroencephalographic signals

A new soft material based in-the-ear EEG recording technique

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