Efficient Epileptic Seizure Prediction Based on Deep Learning

Daoud, Hisham; Bayoumi, Magdy

doi:10.1109/tbcas.2019.2929053

Cited by 326 publications

(189 citation statements)

References 22 publications

Supporting

Mentioning

181

Contrasting

Unclassified

Order By: Relevance

“…In relation to the seizure prediction task, numerous studies have reported that the computational models that learned time and/or frequency domain features observed in pre-ictal state was able to predict the occurrence of seizures at least several minutes before the onset. These models have been mainly trained through supervised learning methods, and they were based on a variety of algorithms ranging from classic machine learning algorithms such as SVM, 66 – 71 k-NN, 71 – 73 hidden Markov model, 74 and etc., to deep learning algorithms such as CNN, 75 – 78 Long Short-Term Memory 70 , 79 (LSTM, a kind of RNN) and their hybrid model, 80 – 82 learning the characteristics of the pre-ictal state distinct from inter-ictal. The developed models have shown sensitivity of 80–90%, but it should be noted that each study had a different prediction time (from 5 minutes before to 1 hour before the onset).…”

Section: Examination Of Epileptic Brain Statesmentioning

confidence: 99%

“…Recent studies have implemented seizure prediction models mainly by employing deep learning algorithms. 75 – 82 Notably, Daoud and Bayoumi 80 have developed a model that predicts seizures 1 hour before the onset, with a high accuracy of 99.6%, using long-term scalp EEG. The proposed model employed both CNN and RNN (especially bidirectional LSTM) to learn the spatial and temporal features respectively from raw EEG data and introduced a semi-supervised learning approach based on the transfer learning technique to reduce training time, showing the potential for real-time usage.…”

Section: Examination Of Epileptic Brain Statesmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence and Computational Approaches for Epilepsy

An¹,

Kang²,

Lee³

2020

J Epilepsy Res

View full text Add to dashboard Cite

Studies on treatment of epilepsy have been actively conducted in multiple avenues, but there are limitations in improving its efficacy due to between-subject variability in which treatment outcomes vary from patient to patient. Accordingly, there is a growing interest in precision medicine that provides accurate diagnosis for seizure types and optimal treatment for an individual epilepsy patient. Among these approaches, computational studies making this feasible are rapidly progressing in particular and have been widely applied in epilepsy. These computational studies are being conducted in two main streams: 1) artificial intelligence-based studies implementing computational machines with specific functions, such as automatic diagnosis and prognosis prediction for an individual patient, using machine learning techniques based on large amounts of data obtained from multiple patients and 2) patient-specific modeling-based studies implementing biophysical in-silico platforms to understand pathological mechanisms and derive the optimal treatment for each patient by reproducing the brain network dynamics of the particular patient per se based on individual patient’s data. These computational approaches are important as it can integrate multiple types of data acquired from patients and analysis results into a single platform. If these kinds of methods are efficiently operated, it would suggest a novel paradigm for precision medicine.

show abstract

Section: Examination Of Epileptic Brain Statesmentioning

confidence: 99%

Section: Examination Of Epileptic Brain Statesmentioning

confidence: 99%

Artificial Intelligence and Computational Approaches for Epilepsy

An¹,

Kang²,

Lee³

2020

J Epilepsy Res

View full text Add to dashboard Cite

show abstract

“…Several theoretical investigations examine the pre-ictal state to find a signature that helps anticipate and predict an epileptiform seizure [22][23][24][25][26]. The authors in [27] presented a pseudo-prospective seizure prediction. A deep learning classifier was trained to distinguish between pre-ictal and interictal signals.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Frontal-Temporal Channels in Epilepsy Seizure Prediction Based on EEMD-ReliefF and DNN

Romney

Manian

2020

Computers

View full text Add to dashboard Cite

Epilepsy patients who do not have their seizures controlled with medication or surgery live in constant fear. The psychological burden of uncertainty surrounding the occurrence of random seizures is one of the most stressful and debilitating aspects of the disease. Despite the research progress in this field, there is a need for a non-invasive prediction system that helps disrupt the seizure epileptiform. Electroencephalogram (EEG) signals are non-stationary, nonlinear and vary with each patient and every recording. Full use of the non-invasive electrode channels is impractical for real-time use. We propose two frontal-temporal electrode channels based on ensemble empirical mode decomposition (EEMD) and Relief methods to address these challenges. The EEMD decomposes the segmented data frame in the ictal state into its intrinsic mode functions, and then we apply Relief to select the most relevant oscillatory components. A deep neural network (DNN) model learns these features to perform seizure prediction and early detection of patient-specific EEG recordings. The model yields an average sensitivity and specificity of 86.7% and 89.5%, respectively. The two-channel model shows the ability to capture patterns from brain locations for non-fontal-temporal seizures.

show abstract

“…The diagnosis of seizures is made by electroencephalogram. Artificial intelligent techniques are being developed to enhance the diagnosis (7).…”

Section: Seizuresmentioning

confidence: 99%