A Deep Learning-Based Real-time Seizure Detection System

Shawki, N.; Elseify, Tarek; Čáp, Tomáš; Shah, Vinit; Obeid, Iyad

doi:10.1109/spmb50085.2020.9353623

Cited by 6 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning-based approaches, such as convolutional neural networks (CNN), recurrent neural networks (RNN), and long short-term memory (LSTM) networks, are potential alternatives in seizure or seizure onset detection 36,37 . Deep learning configurations are superior to the state-of-the-art machine learning models in many fields, ranging from object and activity detection [38][39][40] to language modeling 41 to biological problems [42][43][44] .…”

Section: Seizure / Seizure Onset Detection Algorithmsmentioning

confidence: 99%

“…This study was reproduced in 46 with an empirical observation that 1DCNN performs elegantly for seizure detection using EEG data. Secondly, there are a lot of seizure detection models that use LSTM in the literature 37,[47][48][49] . LSTM offers an elegant choice for seizure classification for time-series data that can exploit the hidden relationship between currently acquired data with the one at previous instants.…”

Section: Seizure / Seizure Onset Detection Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

A deep learning-based automated closed-loop optogenetic system for neuromodulation during seizures

Batabyal

Brodovskaya

Williamson

2022

Preprint

View full text Add to dashboard Cite

Closed-loop electrical brain stimulation is becoming a popular technique proposed for use as a treatment alternative to surgical resection of brain tissue for drug-resistant seizures in epilepsy patients. Closed-loop optogenetic stimulation is an experimental alternative to electrical stimulation since it can stimulate or inhibit neurons. The closed-loop part contains an online seizure detection algorithm, which, based on the design, can detect the onset of a seizure or evaluate a running window for seizure membership. Conventional configurations of closed-loop optogenetics have several limitations, ranging from the adaptability of hardware-based implementation to inadequate, customized feature selection of seizures, among others. Here we provide a detailed description of our closed-loop components. We used a sequential, fully convolutional neural network regressor for complex feature selection of seizures against the controls from local field potential recordings. Our modular design kept the local field potential-recording headset and optical probe separate. The seizure detection and execution of light delivery are fast and can be precisely timed. This automated system is robust to noise, modular in design, flexible in use, and simple execution. When applied in vivo, the proposed work shows efficacy over the state of the arts in terms of improved seizure detection and reduction in false positives.

show abstract

Section: Seizure / Seizure Onset Detection Algorithmsmentioning

confidence: 99%

Section: Seizure / Seizure Onset Detection Algorithmsmentioning

confidence: 99%

A deep learning-based automated closed-loop optogenetic system for neuromodulation during seizures

Batabyal

Brodovskaya

Williamson

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Recently, deep learning methods are being increasingly used for EEG analysis [5], [6]. While monitoring systems built by means of advanced deep learning techniques generally require considerable resources during the training phase, deployment is often not limited by computational power as inference is achievable in real-time [7].…”

Section: Introductionmentioning

confidence: 99%

Capsule Attention for Multimodal EEG-EOG Representation Learning With Application to Driver Vigilance Estimation

Zhang

Etemad

2021

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

Driver vigilance estimation is an important task for transportation safety. Wearable and portable brain-computer interface devices provide a powerful means for real-time monitoring of the vigilance level of drivers to help with avoiding distracted or impaired driving. In this paper, we propose a novel multimodal architecture for in-vehicle vigilance estimation from Electroencephalogram and Electrooculogram. To enable the system to focus on the most salient parts of the learned multimodal representations, we propose an architecture composed of a capsule attention mechanism following a deep Long Short-Term Memory (LSTM) network. Our model learns hierarchical dependencies in the data through the LSTM and capsule feature representation layers. To better explore the discriminative ability of the learned representations, we study the effect of the proposed capsule attention mechanism including the number of dynamic routing iterations as well as other parameters. Experiments show the robustness of our method by outperforming other solutions and baseline techniques, setting a new state-of-the-art. We then provide an analysis on different frequency bands and brain regions to evaluate their suitability for driver vigilance estimation. Lastly, an analysis on the role of capsule attention, multimodality, and robustness to noise is performed, highlighting the advantages of our approach.

show abstract

Co-ReaSON: EEG-based Onset Detection of Focal Epileptic Seizures with Multimodal Feature Representations

Kumar,

Yu,

Wenzel

et al. 2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

A Deep Learning-Based Real-time Seizure Detection System

Cited by 6 publications

References 8 publications

A deep learning-based automated closed-loop optogenetic system for neuromodulation during seizures

A deep learning-based automated closed-loop optogenetic system for neuromodulation during seizures

Capsule Attention for Multimodal EEG-EOG Representation Learning With Application to Driver Vigilance Estimation

Co-ReaSON: EEG-based Onset Detection of Focal Epileptic Seizures with Multimodal Feature Representations

Contact Info

Product

Resources

About