A retrospective glance at automatic detection of epileptic spike in electroencephalogram

Zhou, Jing

doi:10.18088/ejbmr.1.4.2015.pp9-17

Cited by 2 publications

(1 citation statement)

References 67 publications

(161 reference statements)

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“…Epileptiform sharps are detected using a variety of algorithms such as deterministic finite automata (DFA) [ 3 ], geometrical features and artificial neural networks [ 4 ], cross-correlation [ 5 ], and dynamic time warping [ 6 ]. A recent review reported that these algorithms can detect epileptiform sharps with approximately 90% accuracy [ 7 ]. Algorithms for the automatic detection of SSWs have also been introduced in the literature.…”

Section: Introductionmentioning

confidence: 99%

Automatic Identification of Interictal Epileptiform Discharges in Secondary Generalized Epilepsy

Chang

Seung

Lee

et al. 2016

Computational and Mathematical Methods in Medicine

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Ictal epileptiform discharges (EDs) are characteristic signal patterns of scalp electroencephalogram (EEG) or intracranial EEG (iEEG) recorded from patients with epilepsy, which assist with the diagnosis and characterization of various types of epilepsy. The EEG signal, however, is often recorded from patients with epilepsy for a long period of time, and thus detection and identification of EDs have been a burden on medical doctors. This paper proposes a new method for automatic identification of two types of EDs, repeated sharp-waves (sharps), and runs of sharp-and-slow-waves (SSWs), which helps to pinpoint epileptogenic foci in secondary generalized epilepsy such as Lennox-Gastaut syndrome (LGS). In the experiments with iEEG data acquired from a patient with LGS, our proposed method detected EDs with an accuracy of 93.76% and classified three different signal patterns with a mean classification accuracy of 87.69%, which was significantly higher than that of a conventional wavelet-based method. Our study shows that it is possible to successfully detect and discriminate sharps and SSWs from background EEG activity using our proposed method.

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Section: Introductionmentioning

confidence: 99%

Automatic Identification of Interictal Epileptiform Discharges in Secondary Generalized Epilepsy

Chang

Seung

Lee

et al. 2016

Computational and Mathematical Methods in Medicine

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Deep learning for robust detection of interictal epileptiform discharges

et al. 2021

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Objective. Automatic detection of interictal epileptiform discharges (IEDs, short as ‘spikes’) from an epileptic brain can help predict seizure recurrence and support the diagnosis of epilepsy. Developing fast, reliable and robust detection methods for IEDs based on scalp or intracranial electroencephalogram (iEEG) may facilitate online seizure monitoring and closed-loop neurostimulation. Approach. We developed a new deep learning approach, which employs a long short-term memory network architecture (‘IEDnet’) and an auxiliary classifier generative adversarial network (AC-GAN), to train on both expert-annotated and augmented spike events from iEEG recordings of epilepsy patients. We validated our IEDnet with two real-world iEEG datasets, and compared IEDnet with the support vector machine (SVM) and random forest (RF) classifiers on their detection performances. Main results. IEDnet achieved excellent cross-validated detection performances in terms of both sensitivity and specificity, and outperformed SVM and RF. Synthetic spike samples augmented by AC-GAN further improved the detection performance. In addition, the performance of IEDnet was robust with respect to the sampling frequency and noise. Furthermore, we demonstrated the cross-institutional generalization ability of IEDnet while testing between two datasets. Significance. IEDnet achieves excellent detection performances in identifying interictal spikes. AC-GAN can produce augmented iEEG samples to improve supervised deep learning.

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A retrospective glance at automatic detection of epileptic spike in electroencephalogram

Cited by 2 publications

References 67 publications

Automatic Identification of Interictal Epileptiform Discharges in Secondary Generalized Epilepsy

Automatic Identification of Interictal Epileptiform Discharges in Secondary Generalized Epilepsy

Deep learning for robust detection of interictal epileptiform discharges

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