Automated pre-ictal phase detection algorithm from EEG signals

ZeIjkovic, V.; Druzgalski, C.; Yequn, L.; Jian, Cai; Xin, Yangxuan; Bojic, Milena; Mayorga, P.

doi:10.1109/pahce.2013.6568349

Cited by 4 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unlike in [21] where the classification of EEG signals is based purely on two dimensional formed images analysis, here we propose the algorithm that processes and analyzes the one dimensional EEG signals as well two dimensional signals obtained by conversion of EEG signals to images.…”

Section: Pre-ictal State In Eeg Signalsmentioning

confidence: 99%

Pre-Ictal phase detection algorithm based on one dimensional EEG signals and two dimensional formed images analysis

Zeljković

Valev

Tameze

et al. 2013

2013 International Conference on High Performance Computing &Amp; Simulation (HPCS)

View full text Add to dashboard Cite

Over 50 million persons worldwide are affected by epilepsy. It can affect equally young babies as well as old people. Epilepsy is a brain disorder characterized by the neurobiological, cognitive, psychological and social consequences. It is known for sudden, unexpected transitions from normal to pathological behavioral states called epileptic seizures. Epilepsy poses a significant burden to society due to associated healthcare cost to treat and control the unpredictable and spontaneous occurrence of seizures. There is a need for a quick screening process that could help neurologists diagnose and determine the patient's treatment.Electroencephalogram has been traditionally used to diagnose patients by evaluating those brain functions that might correspond to epilepsy. This research focuses on developing new classification technique and the prediction of pre-ictal states that announce epileptic seizures, from the online EEG data analysis. The idea is to place electrodes on critical regions on the patient's head that would wirelessly communicate with the EEG recorder and the unit that performs online automated pre-ictal state detection based on obtained EEG signal. The patient should get timely alert about the possible seizure attack so that she/he can stop with its activities and take safety precautions.

show abstract

Section: Pre-ictal State In Eeg Signalsmentioning

confidence: 99%

Pre-Ictal phase detection algorithm based on one dimensional EEG signals and two dimensional formed images analysis

Zeljković

Valev

Tameze

et al. 2013

2013 International Conference on High Performance Computing &Amp; Simulation (HPCS)

View full text Add to dashboard Cite

show abstract

A Smart Integrated Brain-Computer Interaction for Epileptic Seizure Detection

El-Khamisy

El-Raoof

Youssef

2021

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Epilepsy is brain resulted activities which are affected by suddenly seizures which have unpredictable changes affects brain electrical functionalities. Epilepsy has a significant impact on society on the healthcare treatment, cost, responds, and patients behavior. The study has main objectives to propose accurate integrated framework for epileptic seizure detection from the pre-ictal phase of the EEG signal. Locate the connected channel lobe in region where epileptic is expected to occur. Provide automated and real-time monitoring and send warning messages to patient and epileptologist to take accurate actions before ictal occur. Enable future contribution for different Seizure features and impact. Also reduce cost, time and effort. Based on the hypothesis of entropy of EEG signals during seizure has low value if (n) of channels are detected to have seizure, then they are considered as connected neighbors in brain domain mapping, which is clear alert that patient will have a seizure ictal. This end to end framework has modules of data acquisition, pre-processing, feature extraction, pattern-matching, supports vector machines (SVM) classifier for extracted feature, in addition to monitoring and notification. The extracted features includes lower threshold, homogeneity, weighted permutation entropy, power and energy. Also identify the physiological field located inside the brain which the seizure will expected to occur. The final output results have 92% for True positive rate in addition to 95% of F1 and 98.9% of accuracy. This system has proved consistency during all its phases of seizure detection with valuable and effective support to the society.

show abstract

Pre-ictal phase detection with SVMs

Cózar

Zeljković

González-Linares

et al. 2014

2014 International Conference on High Performance Computing &Amp; Simulation (HPCS)

View full text Add to dashboard Cite

Over 50 million persons worldwide are affected by epilepsy. Epilepsy is a brain disorder known for sudden, unexpected transitions from normal to pathological behavioral states called epileptic seizures. Epilepsy poses a significant burden to society due to associated healthcare cost to treat and control the unpredictable and spontaneous occurrence of seizures. There is a need for a quick screening process that could help neurologist diagnose and determine the patient's treatment. Electroencephalogram has been traditionally used to diagnose patients by evaluating those brain functions that may correspond to epilepsy. The objective of this paper is to implement a novel detection technique of pre-ictal state that announces epileptic seizures from the online EEG data analysis. Unlike most published methods, that are aimed to distinguish only the normal from the epilepsy state, in this work the pre-ictal state is introduced as a new patient status, thus differentiating three possible states: normal (healthy), pre-ictal and epileptic seizure. In this manner, the patient should get timely alert about the possible seizure attack so that she/he can stop with its activities and take safety precautions.

show abstract

Automated pre-ictal phase detection algorithm from EEG signals

Cited by 4 publications

References 19 publications

Pre-Ictal phase detection algorithm based on one dimensional EEG signals and two dimensional formed images analysis

Pre-Ictal phase detection algorithm based on one dimensional EEG signals and two dimensional formed images analysis

A Smart Integrated Brain-Computer Interaction for Epileptic Seizure Detection

Pre-ictal phase detection with SVMs

Contact Info

Product

Resources

About