Absence seizure epilepsy detection using linear and nonlinear eeg analysis methods

Sakkalis, Vangelis; Giannakakis, Giorgos; Farmaki, Christina; Mousas, Abdou; Pediaditis, Matthew; Vorgia, Pelagia; Tsiknakis, Manolis

doi:10.1109/embc.2013.6611002

Cited by 22 publications

(8 citation statements)

References 9 publications

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“…For example, classification into linear and non-linear methods considers variance-based, correlation-based, and simple power spectrum-based methods as linear methods and all other methods as non-linear methods [8][9][10]. We did not adopt this classification as most of the reviewed methods in this paper are non-linear techniques.…”

Section: Introductionmentioning

confidence: 99%

EEG seizure detection and prediction algorithms: a survey

Alshebeili

Alshawi

Ahmad

et al. 2014

EURASIP J. Adv. Signal Process.

231

115

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Epilepsy patients experience challenges in daily life due to precautions they have to take in order to cope with this condition. When a seizure occurs, it might cause injuries or endanger the life of the patients or others, especially when they are using heavy machinery, e.g., deriving cars. Studies of epilepsy often rely on electroencephalogram (EEG) signals in order to analyze the behavior of the brain during seizures. Locating the seizure period in EEG recordings manually is difficult and time consuming; one often needs to skim through tens or even hundreds of hours of EEG recordings. Therefore, automatic detection of such an activity is of great importance. Another potential usage of EEG signal analysis is in the prediction of epileptic activities before they occur, as this will enable the patients (and caregivers) to take appropriate precautions. In this paper, we first present an overview of seizure detection and prediction problem and provide insights on the challenges in this area. Second, we cover some of the state-of-the-art seizure detection and prediction algorithms and provide comparison between these algorithms. Finally, we conclude with future research directions and open problems in this topic.

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Section: Introductionmentioning

confidence: 99%

EEG seizure detection and prediction algorithms: a survey

Alshebeili

Alshawi

Ahmad

et al. 2014

EURASIP J. Adv. Signal Process.

231

115

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show abstract

“…In the related literature, various seizure detection and anticipation algorithms through EEG recordings have been proposed using different approaches [8,[11][12][13]. EEG prediction methods are divided into three main categories, time domain, frequency domain and non linear methods [8,14].…”

Section: Related Workmentioning

confidence: 99%

Absence seizure detection classifying matching pursuit features of EEG signals

Giannakaki¹,

Giannakakis²,

Vorgia³

et al. 2021

EAI Endorsed Transactions on Bioengineering and Bioinformatics

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INTRODUCTION: Absence seizures are characterized by a typical generalized spike-and-wave electroencephalographic (EEG) pattern around 3Hz. The automatic identification of this pattern and consequently its corresponding seizure is a valuable information towards the reliable patient's clinical image and treatment planning. In this paper, we propose a method for absence seizures detection based on EEG signals decomposition via the Matching Pursuit (MP) algorithm. METHODS: Based on the ictal EEG semiology, MP features were extracted able to track the ictal pattern. This analysis was performed in a clinical dataset of 8 pediatric patients (4 females, 4 males) suffering from active absence epilepsy, containing 123 absence seizures in total. Automatic classification schema based on Machine Learning techniques were employed to categorize the MP patterns into non-ictal and ictal states. RESULTS: The seizure detection system achieved a time window based discrimination accuracy of 97.3% by using a Support Vector Machine (SVM) classifier and 10-fold cross-validation, in that way accomplishing a good state of the art performance. DISCUSSION: Compared to other popular spectral analysis methods, Matching Pursuit appears to be a robust and efficient method regarding absence seizures detection on EEG signals and our results indicate that the MP features proposed in this work are features that can be used effectively in seizure detection procedure.

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“…Only a few algorithms have been designed specifically for people with absence seizures. [16][17][18][19][20] In even fewer of these studies, algorithms were designed to run on wearable systems. In contrast to algorithms developed to run in a hospital without strict computational constraints (as they can rely on powerful servers connected to the hospital network), 15 algorithms for wearable systems must meet with strict storage, computing memory, and computing power constraints.…”

Section: Introductionmentioning

confidence: 99%

Computationally-Efficient Algorithm for Real-Time Absence Seizure Detection in Wearable Electroencephalography

Dan

Vandendriessche

Paesschen

et al. 2020

Int. J. Neur. Syst.

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Advances in electroencephalography (EEG) equipment now allow monitoring of people with epilepsy in their daily-life environment. The large volumes of data that can be collected from long-term out-of-clinic monitoring require novel algorithms to process the recordings on board of the device to identify and log or transmit only relevant data epochs. Existing seizure-detection algorithms are generally designed for post-processing purposes, so that memory and computing power are rarely considered as constraints. We propose a novel multi-channel EEG signal processing method for automated absence seizure detection which is specifically designed to run on a microcontroller with minimal memory and processing power. It is based on a linear multi-channel filter that is precomputed offline in a data-driven fashion based on the spatial-temporal signature of the seizure and peak interference statistics. At run-time, the algorithm requires only standard linear filtering operations, which are cheap and efficient to compute, in particular on microcontrollers with a multiply-accumulate unit (MAC). For validation, a dataset of eight patients with juvenile absence epilepsy was collected. Patients were equipped with a 20-channel mobile EEG unit and discharged for a day-long recording. The algorithm achieves a median of 0.5 false detections per day at 95% sensitivity. We compare our algorithm with state-of-the-art absence seizure detection algorithms and conclude it performs on par with these at a much lower computational cost.

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Absence seizure epilepsy detection using linear and nonlinear eeg analysis methods

Cited by 22 publications

References 9 publications

EEG seizure detection and prediction algorithms: a survey

EEG seizure detection and prediction algorithms: a survey

Absence seizure detection classifying matching pursuit features of EEG signals

Computationally-Efficient Algorithm for Real-Time Absence Seizure Detection in Wearable Electroencephalography

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