Epileptic Seizure Detection Using Deep Convolutional Network

Zou, Lang; Liu, Xiaofeng; Jiang, Aimin; Zhousp, Xu

doi:10.1109/icdsp.2018.8631789

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…of distances between consecutive time points of the signal; it is a particularly useful metric in EEG analysis [29]. Beyond these time-domain features, EPViz computes the power within the standard EEG frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-14 Hz), beta (14-30 Hz), and gamma (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45). As shown in Fig 3, the user can control the channel and time interval over which the statistics are computed by moving the red rectangle.…”

Section: Plos Onementioning

confidence: 99%

“…This setting is often cast as a binary classification problem, where the goal is to classify whether short windows (1-10 sec) of multi-channel EEG correspond to baseline or seizure activity [12][13][14]. The methods range from traditional machine learning algorithms applied to hand-crafted features, such as wavelet coefficients [5,[15][16][17][18][19][20][21], spectral power [6,7,[22][23][24][25][26], and non-linear measures [5,17,20,[27][28][29][30][31], to end-to-end deep neural networks based on convolutional and recurrent architectures [32][33][34][35][36][37][38][39][40][41][42][43][44]. Recent work in epilepsy has pivoted towards localizing the seizure onset from EEG, which adds a spatial component to the temporal predictions [23,45,46].…”

Section: Introductionmentioning

confidence: 99%

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EPViz: A flexible and lightweight visualizer to facilitate predictive modeling for multi-channel EEG

et al. 2023

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Scalp Electroencephalography (EEG) is one of the most popular noninvasive modalities for studying real-time neural phenomena. While traditional EEG studies have focused on identifying group-level statistical effects, the rise of machine learning has prompted a shift in computational neuroscience towards spatio-temporal predictive analyses. We introduce a novel open-source viewer, the EEG Prediction Visualizer (EPViz), to aid researchers in developing, validating, and reporting their predictive modeling outputs. EPViz is a lightweight and standalone software package developed in Python. Beyond viewing and manipulating the EEG data, EPViz allows researchers to load a PyTorch deep learning model, apply it to EEG features, and overlay the output channel-wise or subject-level temporal predictions on top of the original time series. These results can be saved as high-resolution images for use in manuscripts and presentations. EPViz also provides valuable tools for clinician-scientists, including spectrum visualization, computation of basic data statistics, and annotation editing. Finally, we have included a built-in EDF anonymization module to facilitate sharing of clinical data. Taken together, EPViz fills a much needed gap in EEG visualization. Our user-friendly interface and rich collection of features may also help to promote collaboration between engineers and clinicians.

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Section: Plos Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

EPViz: A flexible and lightweight visualizer to facilitate predictive modeling for multi-channel EEG

et al. 2023

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Automatic detection of ictal activity in EEG using synchronization and chaos-based attributes

Mahgoub,

Qaraqe

2023

Med Biol Eng Comput

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Automatic seizure onset detectors (SODs) have been proposed to alert epileptic patients when a seizure is about to happen and in turn improve their quality of life. Yet, the detectors proposed in literature are complex and difficult to implement in real-time as they utilize large feature sets with redundant and irrelevant features. Hence, the aim of this work is to propose a simple and lightweight SOD that exploits two characteristics that reflect the neuronal behavior during a seizure. Namely, the synchronization between EEG channels and the chaoticity of the EEG; synchronization was measured by the condition number while the recurrence period density entropy estimated the chaoticity of an EEG signal. A support vector machine was trained and tested on 10 patients from a scalp EEG dataset and was able to detect the considered seizures with a sensitivity of 100% and a false positives rate of 0.5 per hour. The results indicate that synchronization and chaos attributes can reflect the manifestation of seizures in EEG data and can be used to develop SODs. This work emphasizes that even a single relevant feature can produce an SOD with comparable performance to SODs that use many features. Graphical Abstract

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