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Feature Engineering Coupled Machine Learning Algorithms For Epileptic Seizure Forecasting From Intracranial EEGs [article]

Rishav Kumar, Rishi Raj Singh Jhelumi, Achintye Madhav Singh, Prasoon Kumar
2017 bioRxiv   pre-print
The suggested approach was fairly good at prediction of epilepsy in random samples and therefore, it can be used in epileptic seizure forecasting in patients where medication/surgery is ineffective.  ...  Further, the lack of proper forecasting methods for an occurrence of epileptic seizures in epileptic-drug resistant patients or patients not amenable for surgery affects their psychological behaviour and  ...  Acknowledgement The authors wish to acknowledge following entities for their support: -for highlighting the problem and providing a platform to test out the algorithms and -for being  ... 
doi:10.1101/131482 fatcat:3ieumt5jxvd53ij3jt35xvtbsy

Machine learning and wearable devices of the future

Sándor Beniczky, Philippa Karoly, Ewan Nurse, Philippe Ryvlin, Mark Cook
2020 Epilepsia  
Machine learning (ML) is increasingly recognized as a useful tool in healthcare applications, including epilepsy.  ...  There is published evidence for reliable detection of epileptic seizures using implanted electroencephalography (EEG) electrodes and wearable, non-EEG devices.  ...  EN is funded by the 'My Seizure Gauge' grant provided by the Epilepsy Innovation Institute, a research program of the Epilepsy Foundation of America.  ... 
doi:10.1111/epi.16555 pmid:32712958 fatcat:tfhxe7a2mrgapc6voz22wmloaa

Review on Epileptic Seizure Prediction: Machine Learning and Deep Learning Approaches

Milind Natu, Mrinal Bachute, Shilpa Gite, Ketan Kotecha, Ankit Vidyarthi, Deepika Koundal
2022 Computational and Mathematical Methods in Medicine  
Nowadays, modern computational tools, machine learning, and deep learning methods have been used to predict seizures using EEG.  ...  Almost 1% of world's population suffers from epileptic seizures. Prediction of seizures before the beginning of onset is beneficial for preventing seizures by medication.  ...  [45] explained in their paper the importance of machine learning/deep learning with some computational tools used for forecasting epileptic seizures from encephalograms (EEG) signals.  ... 
doi:10.1155/2022/7751263 pmid:35096136 pmcid:PMC8794701 fatcat:mzlwvlfs35djtcqrfaomcqj2fi

Prediction of Seizure Recurrence. A Note of Caution

William J. Bosl, Alan Leviton, Tobias Loddenkemper
2021 Frontiers in Neurology  
Although machine learning is a useful tool for aiding discovery, we believe that the greatest progress will come from deeper understanding of seizures, epilepsy, and the EEG features that enable seizure  ...  Great strides have been made recently in documenting that machine-learning programs can predict seizure occurrence in people who have epilepsy.  ...  Rasheed K, Qayyum A, Qadir J, Sivathamboo S, Kwan P, Kuhlmann L, et al. Machine learning for predicting epileptic seizures using EEG signals: a review.  ... 
doi:10.3389/fneur.2021.675728 pmid:34054713 pmcid:PMC8155381 fatcat:dnsqawwxcjaphoi57g6cd6tk2i

Seizure forecasting and cyclic control of seizures

Rachel E. Stirling, Mark J. Cook, David B. Grayden, Philippa J. Karoly
2020 Epilepsia  
Many advances have been made over the past decade in the field of seizure forecasting, including improvements in algorithms as a result of machine learning and exploration of non-EEG-based measures of  ...  In contrast, seizure forecasts, which theoretically provide the probability of a seizure at any time (as opposed to predicting the next seizure occurrence), may be more feasible.  ...  Acknowledgments The authors acknowledge the contributions of Dr Dean Freestone, Dr Benjamin Brinkmann, Professor Mark Richardson, Professor Andreas Schulze-Bonhage, and the 'My Seizure Gauge' teams for  ... 
doi:10.1111/epi.16541 pmid:32712968 fatcat:4c2v6tlxnnb5thontfyz5umhaa

Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data [article]

Haotian Liu, Lin Xi, Ying Zhao, Zhixiang Li
2019 arXiv   pre-print
However, as the development of computer technology, the application of machine learning introduced new ideas for seizure forecasting.  ...  Applying machine learning model onto the predication of epileptic seizure could help us obtain a better result and there have been plenty of scientists who have been doing such works so that there are  ...  However, as the development of computer technology, the application of machine learning introduced new ideas for seizure forecasting.  ... 
arXiv:1910.02544v1 fatcat:3go76afkvzatxfrafmy43wdn3m

Multi-Channel Vision Transformer for Epileptic Seizure Prediction

Ramy Hussein, Soojin Lee, Rabab Ward
2022 Biomedicines  
Epilepsy is a neurological disorder that causes recurrent seizures and sometimes loss of awareness. Around 30% of epileptic patients continue to have seizures despite taking anti-seizure medication.  ...  In this study, we introduce a Transformer-based approach called Multi-channel Vision Transformer (MViT) for automated and simultaneous learning of the spatio-temporal-spectral features in multi-channel  ...  performance of the proposed MViT approach for epileptic seizure prediction.  ... 
doi:10.3390/biomedicines10071551 pmid:35884859 pmcid:PMC9312955 fatcat:7nuljdhtfnb7tok3p4xfdblapm

Seizure Susceptibility Prediction in Uncontrolled Epilepsy

Nhan Duy Truong, Yikai Yang, Christina Maher, Levin Kuhlmann, Alistair McEwan, Armin Nikpour, Omid Kavehei
2021 Frontiers in Neurology  
We confirm using our tool that interictal slowing activities are a promising biomarker for epileptic seizure susceptibility prediction.  ...  There have been some studies on identifying potential biomarkers for seizure forecasting; however, the questions of "What are the true biomarkers for seizure prediction" or even "Is there a valid biomarker  ...  We also introduce a tool to facilitate the exploration of biomarkers for epileptic seizure forecasting.  ... 
doi:10.3389/fneur.2021.721491 pmid:34589049 pmcid:PMC8474878 fatcat:meic3hiipzgqlckznvyxikqw2e

A machine learning framework for space medicine predictive diagnostics with physiological signals

Ning Wang, Michael R. Lyu, Chenguang Yang
2013 2013 IEEE Aerospace Conference  
Namely, there is an urgent call for an effective onboard medical system to predict and prevent health problems in a timely manner, rather than following reactive approaches which are inherent to conventional  ...  Prognostics and health management (PHM) in the context of space missions focuses on the fundamental issues of system failures in an attempt to predict when the failures may occur, and links these issues  ...  DISEASE PREDICTION WITH MACHINE LEARNING APPROACH This section focuses on our machine learning based disease prediction methodology.  ... 
doi:10.1109/aero.2013.6497431 fatcat:cwmboivdrjhf3cioakc2jssvtu

Components of Soft Computing for Epileptic Seizure Prediction and Detection [chapter]

B. Suguna Nanthini
2019 Epilepsy - Advances in Diagnosis and Therapy [Working Title]  
The classifier with a suitable learning method can perform well for automated epileptic seizure detection systems.  ...  The following sections of this chapter explain the merits and demerits of soft computing and the procedure for automated epileptic seizure prediction and detection.  ...  Computing for Epileptic Seizure Prediction and Detection DOI: Components of Soft Computing for Epileptic Seizure Prediction and Detection DOI:  ... 
doi:10.5772/intechopen.83413 fatcat:rjixze4mbrawpjl2btuajpzose

Seizure Prediction by Graph Mining, Transfer Learning, and Transformation Learning [chapter]

Nimit Dhulekar, Srinivas Nambirajan, Basak Oztan, Bülent Yener
2015 Lecture Notes in Computer Science  
We present in this study a novel approach to predicting EEG epileptic seizures: we accurately model and predict non-ictal cortical activity and use prediction errors as parameters that significantly distinguish  ...  We learn a one-step forecast operator restricted to just these features, using autoregression (AR (1) ).  ...  Autoregressive models (AR) are commonly used tools for time-series prediction, and have been used to capture the spatio-temporal properties of EEG signals [3, 68] .  ... 
doi:10.1007/978-3-319-21024-7_3 fatcat:u66s4nsunjdgbbdgrbuu7mxl3q

Wireless Interfacing with Closed Loop Control for Seizure Prediction

P. Sritha, P.Geethamani
2019 Zenodo  
The primary point of epilepsy treatments is to give seizure control to the patients while taking out symptoms.  ...  Huge numbers of the confinements of current intercession systems have enhanced the specificity of mediation through on-request methodologies may survive.  ...  As of late, a few calculations for seizure expectation have detailed high affectability and specificity with the utilization of great classifiers created in the machine learning network.  ... 
doi:10.5281/zenodo.2560188 fatcat:qxnzjm47uzcclkmrxt7uj3qgem

Using Deep Learning and Machine Learning to Detect Epileptic Seizure with Electroencephalography (EEG) Data

Haotian Liu, Lin Xi, Ying Zhao, Zhixiang Li
2019 Machine Learning Research  
In conclusion, machine learning and deep learning demonstrated their potential usage in epileptic seizure identification using EEG raw data.  ...  Epileptic seizure is associated with significant morbidity diseases and mortality. An early identification of seizure activity can help prevent patients from adverse outcomes.  ...  As the development of computer technology, the application of machine learning Electroencephalography (EEG) Data introduced new ideas for seizure forecasting and recognizing.  ... 
doi:10.11648/j.mlr.20190403.11 fatcat:lujodxoturfs3dwpwpauiz4wgy

Seizure Diaries and Forecasting With Wearables: Epilepsy Monitoring Outside the Clinic

Benjamin H. Brinkmann, Philippa J. Karoly, Ewan S. Nurse, Sonya B. Dumanis, Mona Nasseri, Pedro F. Viana, Andreas Schulze-Bonhage, Dean R. Freestone, Greg Worrell, Mark P. Richardson, Mark J. Cook
2021 Frontiers in Neurology  
However, seizure forecasting requires perpetual use of a device for monitoring, increasing the importance of the system's acceptability to users.  ...  explores the feasibility to detect and forecast impending seizures via long-term use of these systems.  ...  ACKNOWLEDGMENTS The authors thank the My Seizure Gauge team for technical and administrative support.  ... 
doi:10.3389/fneur.2021.690404 fatcat:2dbfsa7warbqljnqsrqvfctvwa

Cycles of self-reported seizure likelihood correspond to yield of diagnostic epilepsy monitoring [article]

Philippa J Karoly, Dominique Eden, Ewan S Nurse, Mark J Cook, Janelle Taylor, Sonya Dumanis, Mark P Richardson, Benjamin H Brinkmann, Dean R Freestone
2020 medRxiv   pre-print
Methods: We used a database of ambulatory vEEG studies to select a cohort with linked electronic seizure diaries of more than 20 reported seizures over at least 8 weeks.  ...  We hypothesized that personalized seizure forecasts could be used to optimize the timing of vEEG and improve diagnostic yield.  ...  In contrast, forecasts based on black-bo machine learning models cannot be projected beyond the range of the available data, so are less fle ible for making longrange estimates of seizure likelihood.  ... 
doi:10.1101/2020.10.05.20207407 fatcat:kbisewgoqfgllolx4sl5i5yqqi
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