Seven Epileptic Seizure Type Classification in Pre-Ictal, Ictal and Inter-Ictal Stages Using Machine Learning Techniques

Dutta, Kusumika Krori; Manohar, Premila; Indira, K.; Naaz, Falalk; Lakhminarayanan, Meenakshi; Rajagopalan, Shwethaa

doi:10.21203/rs.3.rs-2100432/v1

Cited by 2 publications

(2 citation statements)

References 41 publications

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“…The K-nearest neighbor (KNN) algorithm has validated the solution by distinguishing an epilepsy group (EG) against a healthy control (HC). Afterward, a psychogenic kind of disease was tested using another group of patients characterized by non-epileptic disorder (NEAD) [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Seizure stage detection of epileptic seizure using convolutional neural networks

Krori Dutta,

Manohar,

Krishnappa

2024

IJECE

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According to the World Health Organization (WHO), seventy million individuals worldwide suffer from epilepsy, a neurological disorder. While electroencephalography (EEG) is crucial for diagnosing epilepsy and monitoring the brain activity of epilepsy patients, it requires a specialist to examine all EEG recordings to find epileptic behavior. This procedure needs an experienced doctor, and a precise epilepsy diagnosis is crucial for appropriate treatment. To identify epileptic seizures, this study employed a convolutional neural network (CNN) based on raw scalp EEG signals to discriminate between preictal, ictal, postictal, and interictal segments. The possibility of these characteristics is explored by examining how well time-domain signals work in the detection of epileptic signals using intracranial Freiburg Hospital (FH), scalp Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) databases, and Temple University Hospital (TUH) EEG. To test the viability of this approach, two types of experiments were carried out. Firstly, binary class classification (preictal, ictal, postictal each versus interictal) and four-class classification (interictal versus preictal versus ictal versus postictal). The average accuracy for stage detection using CHB-MIT database was 84.4%, while the Freiburg database's time-domain signals had an accuracy of 79.7% and the highest accuracy of 94.02% for classification in the TUH EEG database when comparing interictal stage to preictal stage.

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Section: Introductionmentioning

confidence: 99%

Seizure stage detection of epileptic seizure using convolutional neural networks

Krori Dutta,

Manohar,

Krishnappa

2024

IJECE

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“…A total of nine connectivity features based on five different measures in time, frequency, and timefrequency domains have been tested. The solution has been validated by the K-Nearest Neighbour algorithm, classifying an epilepsy group (EG) vs. a healthy control (HC), and subsequently, with another cohort of patients characterised by non-epileptic attacks (NEAD), a psychogenic type of disorder was tried out [8,9].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Seizure Stages Based on Electroencephalography Signals and Machine Learning

Dutta¹,

Manohar²,

Indira³

2023

Preprint

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Electroencephalography (EEG) is essential for tracking brain activity and identifying seizure effects. However, epileptic behaviour can only be detected after a specialist has carefully analysed all EEG recordings along with a proper history of the patient. A skilled physician is required for the right epilepsy diagnosis and therapy. But most of the time, patients visit the clinician in the interictal stage with no proper history documented. Therefore, it was essential to the automatic prediction of stages of seizure. K nearest neighbours (KNN) and random forest (RF) models using raw EEG signals, preictal, ictal, postictal, and interictal stages were identified in this study. The possibility of these characteristics is explored by examining how well time-domain signals work in the prediction of epileptic stages using intracranial EEG datasets from Freiburg Hospital (FH), Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT), and Temple University Hospital (TUHEEG). To test the viability of this approach, two different types of simulations were carried out on three binary classifications (interictal vs. preictal, interictal vs. ictal, preictal vs. postictal, and interictal vs. postictal), and one four-class problem (interictal vs. preictal vs. ictal vs. postictal) was performed for each model. The average accuracy when using time-domain signals in the FH database was 90.5% and 75.0%; CHB-MIT was 92.87% and 75.9%; and TUHEEG was 94.46% and 76.8%, respectively, for the KNN and RF models.

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Seven Epileptic Seizure Type Classification in Pre-Ictal, Ictal and Inter-Ictal Stages Using Machine Learning Techniques

Cited by 2 publications

References 41 publications

Seizure stage detection of epileptic seizure using convolutional neural networks

Seizure stage detection of epileptic seizure using convolutional neural networks

Prediction of Seizure Stages Based on Electroencephalography Signals and Machine Learning

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