Improving automated diagnosis of epilepsy from EEGs beyond IEDs

Thangavel, Prasanth; Thomas, John; Sinha, Nishant; Peh, Wei Yan; Yuvaraj, Rajamanickam; Cash, Sydney S.; Chaudhari, Rima; Karia, Sagar; Jin, Jing; Rathakrishnan, Rahul; Saini, Vinay; Shah, Nilesh; Srivastava, Rohit; Tan, Yee‐Leng; Westover, Brandon; Dauwels, Justin

doi:10.1088/1741-2552/ac9c93

Cited by 9 publications

(12 citation statements)

References 44 publications

(112 reference statements)

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“…Moreover, our results suggested that the 20–21-s windows achieved the best performance. These findings agreed with Thangavel et al [ 54 ], who classified epileptic signals using different features and examined different window lengths, concluding that the 20-s time window generated some of the best performance results.…”

Section: Discussionsupporting

confidence: 90%

Evaluating the Window Size’s Role in Automatic EEG Epilepsy Detection

Christou

Miltiadous

Tsoulos

et al. 2022

Sensors

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Electroencephalography is one of the most commonly used methods for extracting information about the brain’s condition and can be used for diagnosing epilepsy. The EEG signal’s wave shape contains vital information about the brain’s state, which can be challenging to analyse and interpret by a human observer. Moreover, the characteristic waveforms of epilepsy (sharp waves, spikes) can occur randomly through time. Considering all the above reasons, automatic EEG signal extraction and analysis using computers can significantly impact the successful diagnosis of epilepsy. This research explores the impact of different window sizes on EEG signals’ classification accuracy using four machine learning classifiers. The machine learning methods included a neural network with ten hidden nodes trained using three different training algorithms and the k-nearest neighbours classifier. The neural network training methods included the Broyden–Fletcher–Goldfarb–Shanno algorithm, the multistart method for global optimization problems, and a genetic algorithm. The current research utilized the University of Bonn dataset containing EEG data, divided into epochs having 50% overlap and window lengths ranging from 1 to 24 s. Then, statistical and spectral features were extracted and used to train the above four classifiers. The outcome from the above experiments showed that large window sizes with a length of about 21 s could positively impact the classification accuracy between the compared methods.

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Section: Discussionsupporting

confidence: 90%

Evaluating the Window Size’s Role in Automatic EEG Epilepsy Detection

Christou

Miltiadous

Tsoulos

et al. 2022

Sensors

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“…Recent investigations explored the use of quantitative scalp EEG analysis to assist the diagnosis of epilepsy, mainly based on the use of ML. [54][55][56][57][58][59] For example, SpikeNet, a deep neural network, was trained on a total of 9571 scalp EEG records (with and without spikes) to perform spike detection and showed performances compared to those achieved by fellowship-trained neurophysiology experts. 54 On the other side, DeepSpike was developed for the detection of epileptiform discharges based on multiple instance object detection and required a relatively low number of labeled training data.…”

Section: Scalp Eeg Recordingsmentioning

confidence: 99%

“…Recent investigations explored the use of quantitative scalp EEG analysis to assist the diagnosis of epilepsy, mainly based on the use of ML 54–59 . For example, SpikeNet, a deep neural network, was trained on a total of 9571 scalp EEG records (with and without spikes) to perform spike detection and showed performances compared to those achieved by fellowship‐trained neurophysiology experts 54 .…”

Section: Recent Advances In Quantitative Eeg Analysismentioning

confidence: 99%

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From basic sciences and engineering to epileptology: A translational approach

et al. 2023

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Collaborative efforts between basic scientists, engineers, and clinicians are enabling translational epileptology. In this article, we summarize the recent advances presented at the International Conference for Technology and Analysis of Seizures (ICTALS 2022): (1) novel developments of structural magnetic resonance imaging; (2) latest electroencephalography signal‐processing applications; (3) big data for the development of clinical tools; (4) the emerging field of hyperdimensional computing; (5) the new generation of artificial intelligence (AI)–enabled neuroprostheses; and (6) the use of collaborative platforms to facilitate epilepsy research translation. We highlight the promise of AI reported in recent investigations and the need for multicenter data‐sharing initiatives.

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“…Due to delays in epilepsy diagnosis and untimely and inappropriate treatments, many patients develop serious complications, such as cognitive disorders, emotional disorders, etc. During a seizure, ictal epileptiform discharges (IEDs) are produced, which is a necessary condition for the diagnosis of epilepsy [ 3 ]. However, a short-term interval electroencephalogram (EEG) cannot accurately record the IEDs; therefore, the diagnosis of PWEs is based on video electroencephalogram (VEEG) in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

EEG Microstate Features as an Automatic Recognition Model of High-Density Epileptic EEG Using Support Vector Machine

Liu

et al. 2022

Brain Sciences

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Epilepsy is one of the most serious nervous system diseases; it can be diagnosed accurately by video electroencephalogram. In this study, we analyzed microstate epileptic electroencephalogram (EEG) to aid in the diagnosis and identification of epilepsy. We recruited patients with focal epilepsy and healthy participants from the Third Xiangya Hospital and recorded their resting EEG data. In this study, the EEG data were analyzed by microstate analysis, and the support vector machine (SVM) classifier was used for automatic epileptic EEG classification based on features of the EEG microstate series, including microstate parameters (duration, occurrence, and coverage), linear features (median, second quartile, mean, kurtosis, and skewness) and non-linear features (Petrosian fractal dimension, approximate entropy, sample entropy, fuzzy entropy, and Lempel–Ziv complexity). In the gamma sub-band, the microstate parameters as a model were the best for interictal epilepsy recognition, with an accuracy of 87.18%, recall of 70.59%, and an area under the curve of 94.52%. There was a recognition effect of interictal epilepsy through the features extracted from the EEG microstate, which varied within the 4~45 Hz band with an accuracy of 79.55%. Based on the SVM classifier, microstate parameters and EEG features can be effectively used to classify epileptic EEG, and microstate parameters can better classify epileptic EEG compared with EEG features.

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Improving automated diagnosis of epilepsy from EEGs beyond IEDs

Cited by 9 publications

References 44 publications

Evaluating the Window Size’s Role in Automatic EEG Epilepsy Detection

Evaluating the Window Size’s Role in Automatic EEG Epilepsy Detection

From basic sciences and engineering to epileptology: A translational approach

EEG Microstate Features as an Automatic Recognition Model of High-Density Epileptic EEG Using Support Vector Machine

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