mlsanityData to 7 Oct 2026

Real-Time Seizure Detection using EEG: A Comprehensive Comparison of Recent Approaches under a Realistic Setting

arXiv 2201.08780 ↗First posted 21 Jan 2022cs.LGcited by 8

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Abstract (arXiv)

Electroencephalogram (EEG) is an important diagnostic test that physicians use to record brain activity and detect seizures by monitoring the signals. There have been several attempts to detect seizures and abnormalities in EEG signals with modern deep learning models to reduce the clinical burden. However, they cannot be fairly compared against each other as they were tested in distinct experimental settings. Also, some of them are not trained in real-time seizure detection tasks, making it hard for on-device applications. Therefore in this work, for the first time, we extensively compare multiple state-of-the-art models and signal feature extractors in a real-time seizure detection framework suitable for real-world application, using various evaluation metrics including a new one we propose to evaluate more practical aspects of seizure detection models. Our code is available at https://github.com/AITRICS/EEG_real_time_seizure_detection.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 13 of its 52 references that are in the map. First posted on arXiv 21 Jan 2022.

Builds on

13 of 52 references.

References · 13

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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  7. 2018The Temple University Hospital Seizure Detection Corpuscited by 80
  8. 2018ChronoNet: A Deep Recurrent Neural Network for Abnormal EEG Identificationcited by 27
  9. 2019Neural Memory Networks for Seizure Type Classificationcited by 19
  10. 2017Objective evaluation metrics for automatic classification of EEG eventscited by 13
  11. 2017Automatic Analysis of EEGs Using Big Data and Hybrid Deep Learning Architecturescited by 8
  12. 2018Gated Recurrent Networks for Seizure Detectioncited by 3
1 more
  1. 2019Seizure Type Classification using EEG signals and Machine Learning: Setting a benchmarkcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 2; 2025: 2; 2026: 3.

8 papers.

  1. 2024REST: Efficient and Accelerated EEG Seizure Analysis through Residual State Updatescited by 11
  2. 2026Optimizing EEG Graph Structure for Seizure Detection: An Information Bottleneck and Self-Supervised Learning Approachcited by 3
  3. 2024Daily Physical Activity Monitoring -- Adaptive Learning from Multi-source Motion Sensor Datacited by 2
  4. 2022Self-Supervised Predictive Coding with Multimodal Fusion for Patient Deterioration Prediction in Fine-grained Time Resolutioncited by 1
  5. 2025On the challenges of detecting MCI using EEG in the wildcited by 0
  6. 2025Automated Video-EEG Analysis in Epilepsy Studies: Advances and Challengescited by 0
  7. 2026NeuroCanvas: VLLM-Powered Robust Seizure Detection by Reformulating Multichannel EEG as Imagecited by 0
  8. 2026Reducing the Complexity of Deep Learning Models for EEG Analysis on Wearable Devicescited by 0