ArticleBrain sciences2022
A Feature Extraction Method for Seizure Detection Based on Multi-Site Synchronous Changes and Edge Detection Algorithm.
Article in Brain sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Article
- Endophenotypes of OSA that are responsive to pharmacological agents: a narrative review of trazodone and atomoxetine.Sleep & breathing = Schlaf & Atmung · 2026Review
- Identifying the epileptic network by linking interictal functional and structural connectivity.Scientific reports · 2025Article
- Data-driven exploratory method investigation on the effect of dyslexia education at brain connectivity in Turkish children: a preliminary study.Brain structure & function · 2024Article
- The Role of Rab GTPases in the development of genetic and malignant diseases.Molecular and cellular biochemistry · 2024Review
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8 authors.
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Abstract
Automatic detection of epileptic seizures is important in epilepsy control and treatment, and specific feature extraction assists in accurate detection. We developed a feature extraction method for seizure detection based on multi-site synchronous changes and an edge detection algorithm. We investigated five chronic temporal lobe epilepsy rats with 8- and 12-channel detection sites in the hippocampus and limbic system. Multi-site synchronous changes were selected as a specific feature and implemented as a seizure detection method. For preprocessing, we used magnitude-squared coherence maps and Canny edge detection algorithm to find the frequency band with the most significant change in synchronization and the important channel pairs. In detection, we used the maximal cross-correlation coefficient as an indicator of synchronization and the correlation coefficient curves' average value and standard deviation as two detection features. The method achieved high performance, with an average 96.60% detection rate, 2.63/h false alarm rate, and 1.25 s detection delay. The experimental results show that synchronization is an appropriate feature for seizure detection. The magnitude-squared coherence map can assist in selecting a specific frequency band and channel pairs to enhance the detection result. We found that individuals have a specific frequency band that reflects the most significant synchronization changes, and our method can individually adjust parameters and has good detection performance.
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