Towards accurate prediction of epileptic seizures: A review

Assi, Elie Bou; Nguyen, Dang Khoa; Rihana, Sandy; Sawan, Mohamad

doi:10.1016/j.bspc.2017.02.001

Cited by 183 publications

(93 citation statements)

References 70 publications

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“…Advances in data mining and machine learning in the past few decades have attracted significantly more attention to the application of these techniques in detective and predictive data analytics, especially in health care, medical practices, and biomedical engineering (Kuhlmann et al, 2015;Freestone et al, 5 2015; Xiao et al, 2017;Bou Assi et al, 2017;Kuhlmann et al, 2017;Freestone et al, 2017;Sinha et al, 2017). While the body of available proven knowledge lacks a convincing and comprehensive understanding of the sources of epileptic seizures, some early studies showed the possibility of predicting seemingly unpredictable seizures (Rogowski et al, 1981;Salant et al, 1998).…”

Section: Introductionmentioning

confidence: 99%

Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram

et al. 2018

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Seizure prediction has attracted growing attention as one of the most challenging predictive data analysis efforts to improve the life of patients with drug-resistant epilepsy and tonic seizures. Many outstanding studies have reported great results in providing sensible indirect (warning systems) or direct (interactive neural stimulation) control over refractory seizures, some of which achieved high performance. However, to achieve high sensitivity and a low false prediction rate, many of these studies relied on handcraft feature extraction and/or tailored feature extraction, which is performed for each patient independently. This approach, however, is not generalizable, and requires significant modifications for each new patient within a new dataset. In this article, we apply convolutional neural networks to different intracranial and scalp electroencephalogram (EEG) datasets and propose a generalized retrospective and patient-specific seizure prediction method. We use the short-time Fourier transform on 30-s EEG windows to extract information in both the frequency domain and the time domain. The algorithm automatically generates optimized features for each patient to best classify preictal and interictal segments. The method can be applied to any other patient from any dataset without the need for manual feature extraction. The proposed approach achieves sensitivity of 81.4%, 81.2%, and 75% and a false prediction rate of 0.06/h, 0.16/h, and 0.21/h on the Freiburg Hospital intracranial EEG dataset, the Boston Children's Hospital-MIT scalp EEG dataset, and the American Epilepsy Society Seizure Prediction Challenge dataset, respectively. Our prediction method is also statistically better than an unspecific random predictor for most of the patients in all three datasets.

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Section: Introductionmentioning

confidence: 99%

Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram

et al. 2018

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“…Most of the seizure prediction and detection approaches aims on onset seizure. Intervention before the onset of seizure symptoms could be envisaged with accurate seizure forecasting [1]. The existing system mostly focused on seizure prediction using available dataset and smart seizure detection devices using IoT during ictal state.…”

Section: Epileptic Seizure Forecast: State-of-the-artmentioning

confidence: 99%

“…In future, the early seizure detection techniques can be used to provide an alert to the epileptic patient so that an effective diagnosis can be done before the occurrence of seizure onset. [1] A review of signal processing techniques and classification methods assessed in seizure prediction studies New databases, higher sampling frequencies, adequate preprocessing, electrode selection, and machine-learning considerations may increase seizure forecasting capabilities. [27] This paper presents a review of recent efforts and journal articles on seizure prediction.…”

Section: Latest Reviews In Seizure Detectionmentioning

confidence: 99%

Epilepsy Seizure Detection and Prediction Based on DeviceHive

Nanthini*,

Kavitha,

Sivabalaselvamani

et al. 2019

IJRTE

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— Unpredictable nature of epilepsy, patients only has more need of awareness about precautions and how to handle the occurrence. Epilepsy belongs to have a recurrent seizures tendency throughout the life. Seizure may happen due to number of reasons like tumor, head injury, pregnancy time, genetic etc. It can be curable with proper diagnosis, incurable but controllable with lifelong medication and remaining are uncontrollable that leads to death. Recording of alert symptoms like auras, prodromes and precipitant factors are helped to self-alert the patient, create positive impact on quality of life and increase the efficacy of treatments. The need of enhancing early seizure detection and developing wearable monitoring product with low cost is used to create fear free environment among the affected people. In this connection, my proposed work reviewed on existing and currently available IOT based seizure detection and alert systems feasibility.

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“…However, the ability to accurately identify the pre-seizure state remains elusive. Despite several attempts to identify a specific and unique feature that can be used to predict seizures, no single characteristic has been established as a potential and universal precursor of epileptic seizure activity 5 – 7 .…”

Section: Introductionmentioning

confidence: 99%

Bispectrum Features and Multilayer Perceptron Classifier to Enhance Seizure Prediction

Assi

Gagliano

Rihana

et al. 2018

Sci Rep

Self Cite

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The ability to accurately forecast seizures could significantly improve the quality of life of patients with drug-refractory epilepsy. Prediction capabilities rely on the adequate identification of seizure activity precursors from electroencephalography recordings. Although a long list of features has been proposed, none of these is able to independently characterize the brain states during transition to a seizure. This work assessed the feasibility of using the bispectrum, an advanced signal processing technique based on higher order statistics, as a precursor of seizure activity. Quantitative features were extracted from the bispectrum and passed through two statistical tests to check for significant differences between preictal and interictal recordings. Results showed statistically significant differences (p < 0.05) between preictal and interictal states using all bispectrum-extracted features. We used normalized bispectral entropy, normalized bispectral squared entropy, and mean of magnitude as inputs to a 5-layer multilayer perceptron classifier and achieved respective held-out test accuracies of 78.11%, 72.64%, and 73.26%.

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Towards accurate prediction of epileptic seizures: A review

Cited by 183 publications

References 70 publications

Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram

Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram

Epilepsy Seizure Detection and Prediction Based on DeviceHive

Bispectrum Features and Multilayer Perceptron Classifier to Enhance Seizure Prediction

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