Locating ictal activities over human scalp with automated detection using EEG signals

Swami, Piyush; Gandhi, Tapan Kumar; Panigrahi, Bijaya Ketan; Bhatia, M. P. S.; Anand, Sneh

doi:10.1109/spin.2016.7566767

Cited by 14 publications

(12 citation statements)

References 19 publications

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“…Studies show that the mean phase coherence [5] as a measure of phase synchronization between the EEG channels changes significantly during the pre-ictal stage when the brain waves shows signs of seizure formation as compared to the inter-ictal stage. This change in phase synchronization was found to occur before the actual seizure of seizure.…”

Section: Methodsmentioning

confidence: 99%

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Prediction of Epileptic Seizures based on Mean Phase Coherence

Aggarwal

Gandhi²

2017

Preprint

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Abstract-Around 1-2 % of the world's population suffers from epilepsy and 20 % of that vulnerable mass can't be cured through surgery or medicine. Epileptic seizures often occur unpredictably and may cause serious damage to the patient in adverse situations, for e.g. getting a seizure while driving or crossing a road can be fatal. In such a scenario, a reliable mechanism to predict the onset of seizure beforehand is much desirable. In this study, A reliable real-time technique for prediction of epileptic seizures is presented. Mean Phase Coherence (MPC), as a measure of phase synchronization, is used to predict impending seizures in a multi-channel EEG data. It was found that during the pre-ictal stages, MPC values and thus phase synchronization between various channels was found to drop significantly below the level in a non-ictal EEG signal. The range of the prediction horizon for seizures varied from 4-10 mins and prediction of impending seizure attack is possible for 8 out of 10 test cases.

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Section: Methodsmentioning

confidence: 99%

“…Expert models have been built for automated detection of seizures from EEG signature with high accuracy [4], [5]. Techniques such as Neural Networks [6] and k-NN classifier [7] are easily able to classify EEG signatures to ictal or nonictal states.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Epileptic Seizures based on Mean Phase Coherence

Aggarwal

Gandhi²

2017

Preprint

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“…These are segmented EEG time series data collected from ten epilepsy patients obtained from the Neurology Sleep Centre in Hauz Khas in New Delhi. This dataset was created by Swami et al [165]. The data was collected using the AS40 Amplifier at a sample rate of 200 Hz.…”

Section: Eeg Epilepsy Dataset (Hauz Khas)mentioning

confidence: 99%

AI-Based Epileptic Seizure Detection and Prediction in Internet of Healthcare Things: A Systematic Review

et al. 2023

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Epilepsy is a neurological condition affecting around 50 million individuals worldwide, reported by the World Health Organization. This is identified as a hypersensitive disease by clinical associations. The unique characteristics of Electroencephalography have proven to be stable and universal; therefore, researchers have a lot of credibilities. So, it is the most used test for Epileptic Seizure detection and prediction. This study examines the contributions that have so far been made utilizing Electroencephalography technology to detect, predict, and monitor Epileptic Seizures. We have reviewed around 56 research articles, and those papers are selected from different academic databases. The studies explored various approaches, including Machine Learning, Deep Learning, and the Internet of Things framework. A comprehensive discussion of different classification algorithms is analyzed, and their performances are explored. Furthermore, various open issues of the stated approach are discussed, and potential future works are addressed.

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“…Available link. [20] This database comprises of 5.12 seconds EEG data. It was recorded using 57 EEG channel Grass Tele-factor Comet AS40 Amplification System; sampled at 200 Hz.…”

Section: Widespread Eeg Changes Precede Focalmentioning

confidence: 99%

EEG Datasets in Machine Learning Applications of Epilepsy Diagnosis and Seizure Detection

2023

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More than 50 million individuals are affected by epilepsy, a chronic neurological disorder characterized by unprovoked, recurring seizures and psychological symptoms. Researchers are working to automatically detect or predict epileptic episodes through Electroencephalography (EEG) signal analysis, and machine, and deep learning methods. Good quality, open-source, and free EEG data acts as a catalyst in this ongoing battle to manage this disease. This article presents 40+ publicly available EEG datasets for adult and pediatric human populations from 2001-2023. A comparative analysis and discussion on open and private EEG datasets have been done based on objective parameters in this domain. Bonn and CHB-MIT remain the benchmark datasets used for the automatic detection of epileptic and seizure EEG signals. Meta-data has also been released for large EEG data like CHB-MIT. This article will be updated every year to report the progress and changing trends in the development of EEG datasets in this field.

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Locating ictal activities over human scalp with automated detection using EEG signals

Cited by 14 publications

References 19 publications

Prediction of Epileptic Seizures based on Mean Phase Coherence

Prediction of Epileptic Seizures based on Mean Phase Coherence

AI-Based Epileptic Seizure Detection and Prediction in Internet of Healthcare Things: A Systematic Review

EEG Datasets in Machine Learning Applications of Epilepsy Diagnosis and Seizure Detection

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