Seizure Prediction Theory
Problem Statement:
Seizure prediction in humans through processing of scalp/intracranial EEG recording using machine learning algorithms to aid patients with epilepsy to take precautions to mitigate impact of seizures.
Background:
Epilepsy is a neurological condition which affects the nervous system. It is also known as seizure disorder. Seizures are caused by disturbances in the electrical activity of the brain [1]. Approximately 50 million people worldwide have epilepsy, making it one of the most common neurological diseases. The estimated proportion of the general population with active epilepsy (i.e. continuing seizures or with the need for treatment) at a given time is between 4 and 10 per 1000 people [2]. Seizures are recurrent even …show more content…
Nearly all published seizure prediction studies have been retrospective [4].
Epileptic seizures always start in the brain and caused by disturbed brain activity which makes EEG recording a potential basis for seizure prediction. Many researchers worked on EEG based seizure prediction which showed promising results for some classes of patients. The fact that seizures do not have typical EEG pattern in all patients makes it difficult to devise a universal algorithm for seizure prediction.
Researchers have devised algorithms to forecast seizures using models based on correlation dimension, phase synchronization, entropy, time/frequency and other characteristics of EEG for feature extraction and one or more linear/non-linear classifier method to differentiate between pre-ictal and ictal1 stage.
Williamson et al. [6] used EEG spatiotemporal correlation structure for seizure prediction. This method is patient-specific using combination of machine learning and multivariate features. Some researchers presented an Ngram pattern recognition based algorithm to predict seizures …show more content…
Zheng, Yang, Gang Wang, Kuo Li, Gang Bao, and Jue Wang. "Epileptic seizure prediction using phase synchronization based on bivariate empirical mode decomposition." Clinical Neurophysiology 125, no. 6 (2014): 1104-1111.
10. http://www.scholarpedia.org/article/Seizure_prediction#A_Brief_History_of_Seizure_Prediction
Reference for current state-of-the-art:
11. Cook, Mark J., Terence J. O'Brien, Samuel F. Berkovic, Michael Murphy, Andrew Morokoff, Gavin Fabinyi, Wendyl D'Souza et al. "Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study." The Lancet Neurology 12, no. 6 (2013): 563-571.
12. Brinkmann, Benjamin H., Joost Wagenaar, Drew Abbot, Phillip Adkins, Simone C. Bosshard, Min Chen, Quang M. Tieng et al. "Crowdsourcing reproducible seizure forecasting in human and canine epilepsy." Brain 139, no. 6 (2016): 1713-1722.
13. Karoly, Philippa J., Dean R. Freestone, Ray Boston, David B. Grayden, David Himes, Kent Leyde, Udaya Seneviratne, Samuel Berkovic, Terence O’Brien, and Mark J. Cook. "Interictal spikes and epileptic seizures: their relationship and underlying rhythmicity." Brain (2016):
Seizure prediction in humans through processing of scalp/intracranial EEG recording using machine learning algorithms to aid patients with epilepsy to take precautions to mitigate impact of seizures.
Background:
Epilepsy is a neurological condition which affects the nervous system. It is also known as seizure disorder. Seizures are caused by disturbances in the electrical activity of the brain [1]. Approximately 50 million people worldwide have epilepsy, making it one of the most common neurological diseases. The estimated proportion of the general population with active epilepsy (i.e. continuing seizures or with the need for treatment) at a given time is between 4 and 10 per 1000 people [2]. Seizures are recurrent even …show more content…
Nearly all published seizure prediction studies have been retrospective [4].
Epileptic seizures always start in the brain and caused by disturbed brain activity which makes EEG recording a potential basis for seizure prediction. Many researchers worked on EEG based seizure prediction which showed promising results for some classes of patients. The fact that seizures do not have typical EEG pattern in all patients makes it difficult to devise a universal algorithm for seizure prediction.
Researchers have devised algorithms to forecast seizures using models based on correlation dimension, phase synchronization, entropy, time/frequency and other characteristics of EEG for feature extraction and one or more linear/non-linear classifier method to differentiate between pre-ictal and ictal1 stage.
Williamson et al. [6] used EEG spatiotemporal correlation structure for seizure prediction. This method is patient-specific using combination of machine learning and multivariate features. Some researchers presented an Ngram pattern recognition based algorithm to predict seizures …show more content…
Zheng, Yang, Gang Wang, Kuo Li, Gang Bao, and Jue Wang. "Epileptic seizure prediction using phase synchronization based on bivariate empirical mode decomposition." Clinical Neurophysiology 125, no. 6 (2014): 1104-1111.
10. http://www.scholarpedia.org/article/Seizure_prediction#A_Brief_History_of_Seizure_Prediction
Reference for current state-of-the-art:
11. Cook, Mark J., Terence J. O'Brien, Samuel F. Berkovic, Michael Murphy, Andrew Morokoff, Gavin Fabinyi, Wendyl D'Souza et al. "Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study." The Lancet Neurology 12, no. 6 (2013): 563-571.
12. Brinkmann, Benjamin H., Joost Wagenaar, Drew Abbot, Phillip Adkins, Simone C. Bosshard, Min Chen, Quang M. Tieng et al. "Crowdsourcing reproducible seizure forecasting in human and canine epilepsy." Brain 139, no. 6 (2016): 1713-1722.
13. Karoly, Philippa J., Dean R. Freestone, Ray Boston, David B. Grayden, David Himes, Kent Leyde, Udaya Seneviratne, Samuel Berkovic, Terence O’Brien, and Mark J. Cook. "Interictal spikes and epileptic seizures: their relationship and underlying rhythmicity." Brain (2016):