ArticleSensors (Basel, Switzerland)2023
Automatic Seizure Detection Based on Stockwell Transform and Transformer.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 27 citations in OpenAlex.
- CosFNet: A Lightweight Epileptic EEG Detection Model Based on Cosine Convolution and FNet.Bioengineering (Basel, Switzerland) · 2026Article
- Power and Phase Fusion Spectrogram with Three-Dimensional Convolution and Vision Transformer for Seizure Detection.Diagnostics (Basel, Switzerland) · 2026Article
- Multiscale Cosine Convolution Neural Network for Robust and Interpretable Epileptic EEG Detection.Biosensors · 2026Article
- SOP: Selective Orthogonal Projection for Composed Image Retrieval.Sensors (Basel, Switzerland) · 2026Article
- CNN-Autoformer: Automated EEG-Based Seizure Detection and Localization Using Hybrid Deep Learning.Biomedical signal processing and control · 2026Article
- Montage-guided fusion network for long-term scalp electroencephalography seizure detection.Frontiers in neuroinformatics · 2026Article
- Epileptic Seizure Detection Using Machine Learning: A Systematic Review and Meta-Analysis.Brain sciences · 2025Review
- Improved EEG-Based Emotion Classification via Stockwell Entropy and CSP Integration.Entropy (Basel, Switzerland) · 2025Article
- From data to diagnosis: An innovative approach to epilepsy prediction with CGTNet incorporating spatio-temporal features.PloS one · 2025Article
- Auditing cognitive drift in AI-driven recommendation: a responsible AI methods protocol with a health case demonstration.Frontiers in neuroscience · 2025Article
- Non-Invasive Biosensing for Healthcare Using Artificial Intelligence: A Semi-Systematic Review.Biosensors · 2024Review
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Epilepsy is a chronic neurological disease associated with abnormal neuronal activity in the brain. Seizure detection algorithms are essential in reducing the workload of medical staff reviewing electroencephalogram (EEG) records. In this work, we propose a novel automatic epileptic EEG detection method based on Stockwell transform and Transformer. First, the S-transform is applied to the original EEG segments, acquiring accurate time-frequency representations. Subsequently, the obtained time-frequency matrices are grouped into different EEG rhythm blocks and compressed as vectors in these EEG sub-bands. After that, these feature vectors are fed into the Transformer network for feature selection and classification. Moreover, a series of post-processing methods were introduced to enhance the efficiency of the system. When evaluating the public CHB-MIT database, the proposed algorithm achieved an accuracy of 96.15%, a sensitivity of 96.11%, a specificity of 96.38%, a precision of 96.33%, and an area under the curve (AUC) of 0.98 in segment-based experiments, along with a sensitivity of 96.57%, a false detection rate of 0.38/h, and a delay of 20.62 s in event-based experiments. These outstanding results demonstrate the feasibility of implementing this seizure detection method in future clinical applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.