A robust deep learning approach for automatic classification of seizures against non-seizures

Yao, Xinghua; Li, Xiaojin; Ye, Qiang; Huang, Yan; Cheng, Qiang; Zhang, Guoqiang

doi:10.1016/j.bspc.2020.102215

Cited by 81 publications

(34 citation statements)

References 33 publications

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“…The identified seizure focus was: medial temporal (4 subjects), mesial + temporal pole onset (3 subjects), temporal plus (5 subjects), with the plus representing additional seizure foci (orbitofrontal or insula or suprasylvian operculum) (27). The seizure types were: ES (19), FAS (24), FIAS (28), and FBTCS (6).…”

Section: Clinico-demographic Details Of Subjectsmentioning

confidence: 99%

Time-Series Generative Adversarial Network Approach of Deep Learning Improves Seizure Detection From the Human Thalamic SEEG

et al. 2022

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Seizure detection algorithms are often optimized to detect seizures from the epileptogenic cortex. However, in non-localizable epilepsies, the thalamus is frequently targeted for neuromodulation. Developing a reliable seizure detection algorithm from thalamic SEEG may facilitate the translation of closed-loop neuromodulation. Deep learning algorithms promise reliable seizure detectors, but the major impediment is the lack of larger samples of curated ictal thalamic SEEG needed for training classifiers. We aimed to investigate if synthetic data generated by temporal Generative Adversarial Networks (TGAN) can inflate the sample size to improve the performance of a deep learning classifier of ictal and interictal states from limited samples of thalamic SEEG. Thalamic SEEG from 13 patients (84 seizures) was obtained during stereo EEG evaluation for epilepsy surgery. Overall, TGAN generated synthetic data augmented the performance of the bidirectional Long-Short Term Memory (BiLSTM) performance in classifying thalamic ictal and baseline states. Adding synthetic data improved the accuracy of the detection model by 18.5%. Importantly, this approach can be applied to classify electrographic seizure onset patterns or develop patient-specific seizure detectors from implanted neuromodulation devices.

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Section: Clinico-demographic Details Of Subjectsmentioning

confidence: 99%

Time-Series Generative Adversarial Network Approach of Deep Learning Improves Seizure Detection From the Human Thalamic SEEG

et al. 2022

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“…a new use for a drug or a new disease phenotype). ML methods can take several forms (Table 2) and the models themselves can be of various types (Table 3), [6][7][8][9][10][11][12][13][14][15][16] with supervised learning models being the main focus of this primer. ML applications will, in time, become embedded within most EMR, imaging devices, wearable sensors and triage and messaging systems.…”

Section: Aims and Tasks Of MLmentioning

confidence: 99%

Demystifying machine learning: a primer for physicians

Scott

2021

Internal Medicine Journal

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Machine learning is a tool for analysing digitised data sets and formulating predictions that can optimise clinical decision‐making. It aims to identify complex patterns in large data sets and encode them into models that can then classify new unseen cases or make predictions on new data. Machine learning methods take several forms and individual models can be of many different types. More than 50 models have been approved for use in routine healthcare, and the numbers continue to grow exponentially. The reliability and robustness of any model depends on multiple factors, including the quality and quantity of the data used to develop the models, and the selection of features in the data considered most important to maximising accuracy. In ensuring models are safe, effective and reproducible in routine care, physicians need to have some understanding of how these models are developed and evaluated, and to collaborate with data and computer scientists in their design and validation. This narrative review introduces principles, methods and examples of machine learning in a way that does not require mastery of highly complex statistical and computational concepts.

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“…EEG is a commonly used non-invasive auxiliary method in the clinical diagnosis of epilepsy. However, it is a highly tedious, laborious, time-consuming, and costly task for neurologists to identify seizures from EEG for a long time ( Yao et al, 2021 ). Therefore, it is necessary to develop a reliable epilepsy automatic detection system, which can significantly improve the quality of life of epilepsy patients ( Solaija et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Minireview of Epilepsy Detection Techniques Based on Electroencephalogram Signals

Liu

Xiao

et al. 2021

Front. Syst. Neurosci.

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Epilepsy is one of the most common neurological disorders typically characterized by recurrent and uncontrollable seizures, which seriously affects the quality of life of epilepsy patients. The effective tool utilized in the clinical diagnosis of epilepsy is the Electroencephalogram (EEG). The emergence of machine learning promotes the development of automated epilepsy detection techniques. New algorithms are continuously introduced to shorten the detection time and improve classification accuracy. This minireview summarized the latest research of epilepsy detection techniques that focused on acquiring, preprocessing, feature extraction, and classification of epileptic EEG signals. The application of seizure prediction and localization based on EEG signals in the diagnosis of epilepsy was also introduced. And then, the future development trend of epilepsy detection technology has prospected at the end of the article.

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A robust deep learning approach for automatic classification of seizures against non-seizures

Cited by 81 publications

References 33 publications

Time-Series Generative Adversarial Network Approach of Deep Learning Improves Seizure Detection From the Human Thalamic SEEG

Time-Series Generative Adversarial Network Approach of Deep Learning Improves Seizure Detection From the Human Thalamic SEEG

Demystifying machine learning: a primer for physicians

Minireview of Epilepsy Detection Techniques Based on Electroencephalogram Signals

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