Preprocessing effects of 22 univariate features on the performance of seizure prediction methods

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Year 2016 : 7 citations

 Parvez, M.Z. and Paul, M., 2016. Epileptic seizure prediction by exploiting spatiotemporal relationship of EEG signals using phase correlation. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 24(1), pp.158-168.

 Fergus, P., Hussain, A., Hignett, D., Al-Jumeily, D., Abdel-Aziz, K. and Hamdan, H., 2016. A machine learning system for automated whole-brain seizure detection. Applied Computing and Informatics, 12(1), pp.70-89.

 Bhardwaj, A., Tiwari, A., Krishna, R. and Varma, V., 2016. A novel genetic programming approach for epileptic seizure detection. Computer methods and programs in biomedicine, 124, pp.2-18.

 Gómez, C., Poza, J., Gutiérrez, M.T., Prada, E., Mendoza, N. and Hornero, R., 2016. Characterization of EEG patterns in brain-injured subjects and controls after a Snoezelen® intervention. computer methods and programs in biomedicine, 136, pp.1-9.

 Ghaderyan, P. and Abbasi, A., 2016. An efficient automatic workload estimation method based on electrodermal activity using pattern classifier combinations. International Journal of Psychophysiology, 110, pp.91-101.

 Shiao, H.T., Cherkassky, V., Lee, J., Veber, B., Patterson, N., Brinkmann, B. and Worrell, G., 2016. SVM-Based System for Prediction of Epileptic Seizures from iEEG Signal. IEEE Transactions on Biomedical Engineering.

 Babu, U.R. and Sridhar, C.N.V., Design and Classification of EEG and ECG Signals for Detection of Seizures based on Prototype Recognition.

Year 2015 : 15 citations

 Automatic Epileptic Seizure Detection Using Scalp EEG and Advanced Artificial Intelligence Techniques
P Fergus, D Hignett, A Hussain, D Al-Jumeily… - BioMed research …, 2015 - hindawi.com
The epilepsies are a heterogeneous group of neurological disorders and syndromes
characterised by recurrent, involuntary, paroxysmal seizure activity, which is often
associated with a clinicoelectrical correlate on the electroencephalogram. The diagnosis ...

 An Innovative Genetic Programming Framework in modelling a real time Epileptic Seizure detection system
A Bhardwaj, A Tiwari, M RameshKrishna… - 2015 - ase360.org
Epilepsy, sometimes called seizure disorder, is a neurological condition that substantiates
itself as a susceptibility to seizures. A seizure is a sudden burst of rhythmic discharges of
electrical activity in the brain that causes an alteration in behavior, sensation, or ...

 Reliable seizure prediction from EEG data
V Cherkassky, B Veber, J Lee, HT Shiao… - … Joint Conference on, 2015 - ieeexplore.ieee.org
Abstract-There is a growing interest in data-analytic modeling for prediction and/or detection
of epileptic seizures from EEG recording of brain activity [1-10]. Even though there is clear
evidence that many patients have changes in EEG signal prior to seizures, development ...
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 Band-sensitive seizure onset detection via CSP-enhanced EEG features
M Qaraqe, M Ismail, E Serpedin - Epilepsy & Behavior, 2015 - Elsevier
Abstract This paper presents two novel epileptic seizure onset detectors. The detectors rely
on a common spatial pattern (CSP)-based feature enhancement stage that increases the
variance between seizure and nonseizure scalp electroencephalography (EEG). The ...

 Machine learning for seizure prediction: A revamped approach
A Sai Kumar, L Nigam, D Karnam… - Advances in …, 2015 - ieeexplore.ieee.org
Abstract—Occurrence of multiple seizures is a common phenomenon observed in patients
with epilepsy: a neurological malfunction that affects approximately 50 million people in the
world. Seizure prediction is widely acknowledged as an important problem in the ...

 A novel genetic programming approach for epileptic seizure detection
A Bhardwaj, A Tiwari, R Krishna, V Varma - Computer methods and …, 2015 - Elsevier
Abstract The human brain is a delicate mix of neurons (brain cells), electrical impulses and
chemicals, known as neurotransmitters. Any damage has the potential to disrupt the
workings of the brain and cause seizures. These epileptic seizures are the manifestations ...

 [PDF] A Machine Learning System for Automated Whole-Brain Seizure Detection
B Street, P de Moulon - 2015 - researchgate.net
ABSTRACT Epilepsy is a chronic neurological condition that affects approximately 70 million
people worldwide. Characterised by sudden bursts of excess electricity in the brain,
manifesting as seizures, epilepsy is still not well understood when compared with other ...

 Epileptic Seizure Prediction by Exploiting Spatiotemporal Relationship of EEG Signals using Phase Correlation
MZ Parvez, M Paul - 2015 - ieeexplore.ieee.org
Abstract—Automated seizure prediction has a potential in epilepsy monitoring, diagnosis,
and rehabilitation. Electroencephalogram (EEG) is widely used for seizure detection and
prediction. This paper proposes a new seizure prediction approach based on ...

 [HTML] A machine learning system for automated whole-brain seizure detection
P Fergus, A Hussain, D Hignett, D Al-Jumeily… - Applied Computing and …, 2015 - Elsevier
Abstract Epilepsy is a chronic neurological condition that affects approximately 70 million
people worldwide. Characterised by sudden bursts of excess electricity in the brain,
manifesting as seizures, epilepsy is still not well understood when compared with other ...

 [PDF] Detection of Pre-stage of Epileptic Seizure by Exploiting Temporal Correlation of EMD Decomposed EEG Signals
MZ Parvez, M Paul, M Antolovich - Journal of Medical and Bioengineering …, 2015 - jomb.org
Abstract—Epilepsy is one of the common neurological disorders characterized by a sudden and
recurrent malfunction of the brain that is termed “seizure”, affecting over 50 million individuals
worldwide. The Electroencephalogram (EEG) is the most influential technique in ...

 Seizure prediction by analyzing EEG signal based on phase correlation
MZ Parvez, M Paul - … in Medicine and Biology Society (EMBC), …, 2015 - ieeexplore.ieee.org
Abstract—Epilepsy is a common neurological disorders characterized by sudden recurrent
seizures. Electroencephalogram (EEG) is widely used to diagnose possible epileptic
seizure. Many research works have been devoted to predict epileptic seizure by analyzing ...

 MZ Parvez, M Paul - csusap.csu.edu.au
ABSTRACT Epilepsy is one of the common neurological disorders characterized by a
sudden and recurrent malfunction of the brain that is termed “seizure”, affecting around 65
million individuals worldwide. Epileptic seizure may lead to many injuries such as ...

 [PDF] An Enhanced Wavelet Neural Network Model with Metaheuristic Harmony Search Algorithm for Epileptic Seizure Prediction
Z Zainuddin, KH Lai, P Ong - International Journal of Modeling and …, 2015 - ijmo.org
Abstract—The task of epileptic seizure prediction aims at differentiating between two classes
of electroencephalography (EEG) signals, namely interictal and pre-ictal signals. The
development of an automated classifier that is capable of performing such task with high ...

 [PDF] Epileptic Seizure Prediction by Exploiting Signal Transitions Phenomena
MZ Parvez, M Paul - waset.org
Abstract—A seizure prediction method is proposed by extracting global features using
phase correlation between adjacent epochs for detecting relative changes and local
features using fluctuation/deviation within an epoch for determining fine changes of ...

 Parvez, Mohammad Zavid, Manoranjan Paul, and Michael Antolovich. "Detection of Pre-stage of Epileptic Seizure by Exploiting Temporal Correlation of EMD Decomposed EEG Signals." Journal of Medical and Bioengineering Vol 4.2 (2015).

Year 2014 : 3 citations

 Parvez, Mohammad Zavid, Manoranjan Paul, and Michael Antolovich. "Detection of Pre-stage of Epileptic Seizure by Exploiting Temporal Correlation of EMD Decomposed EEG Signals." Journal of Medical and Bioengineering Vol 4.2 (2015).

 Bhardwaj, Arpit, et al. "Classification of EEG signals using a novel genetic programming approach." Proceedings of the 2014 conference companion on Genetic and evolutionary computation companion. ACM, 2014.

 Ghaderyan, Peyvand, Ataollah Abbasi, and Mohammad Hossein Sedaaghi. "An efficient seizure prediction method using KNN-based undersampling and linear frequency measures." Journal of neuroscience methods (2014).

Year 2013 : 1 citations

 Prediction and Detection of Epileptic Seizure by Analysing EEG Signals
MZ Parvez, M Paul - researchgate.net