---
title: "NED-Net: iterative annotation-training for rodent EEG seizure detection"
id: "biorxiv-18-ned-net-an-annotation-training-loop-for-rodent-eeg-event-detection"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-18-ned-net-an-annotation-training-loop-for-rodent-eeg-event-detection"
content_type: "clinical_feed_article"
specialty: "Neurology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.10.15.759362v1?rss=1"
published_at: "2026-10-21T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# NED-Net: iterative annotation-training for rodent EEG seizure detection
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-18-ned-net-an-annotation-training-loop-for-rodent-eeg-event-detection
- **Specialty:** [Neurology](https://medichelpline.com/clinical-feed/neurology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.10.15.759362v1?rss=1)
- **Published At:** 2026-10-21T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The preprint introduces **NED-Net**, open-source software that creates an annotation–training loop for automated rodent EEG event detection and ongoing detector maintenance. - The tool ingests human-reviewed candidate events as training labels, runs live detection during recording, and retrains models as annotations accumulate to adapt to local recording conditions. - A detector trained on one mouse cohort (867 annotated seizures) produced 171 detections; human review found 93.6% precision among those reviewed, according to the authors. - When the same detector was applied unchanged to a second cohort on the same acquisition system, 12 of 13 reviewed detections were seizures, but per-animal **recall** varied widely (median reported at 10%), and four of 11 animals had no convulsive reference seizures detected. - Retraining the detector with annotations from the second cohort increased convulsive seizure recall from 15% to 64% and yielded at least one reference seizure detected in every animal in that cohort. - On recordings withheld from training, the retrained system achieved 75% of detections classified as seizures and detected 14 of 22 reference seizures; the original source model and a model trained without those animals detected none of those withheld reference seizures. - The authors provide links to code and data (GitHub and Zenodo) and note that NED-Net is intended to support evaluating and adapting detectors to local laboratory recordings. - The manuscript is a preprint and has not undergone peer review; the source did not report some methodological details such as precise model architecture, hyperparameters, or the full annotation workflow in this summary. - Funding sources are listed; the authors declared no competing interests.
## Clinical Analysis & Structured Key Points
NED-Net: an annotation-training loop for rodent EEG event detection | bioRxiv Skip to main content New Results NED-Net: an annotation-training loop for rodent EEG event detection Mohd Yaqub MIR , View ORCID Profile Marco Ledri doi: https://doi.org/10.64898/2026.10.15.759362 Mohd Yaqub MIR Epilepsy Center, Lund University Find this author on Google Scholar Find this author on PubMed Search for this author on this site Marco Ledri Epilepsy Center, Lund University Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Marco Ledri For correspondence: marco.ledri{at}med.lu.se Abstract Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Long-term EEG monitoring is central to rodent epilepsy research, and manual review is laborious because recordings span hundreds to tens of thousands of animal-hours. Published automated detectors perform well on their development datasets but are typically fixed models that may not adapt to another laboratory's electrodes, animals, or noise conditions. We introduce NED-Net, open-source software for training and maintaining seizure detectors. It uses reviewed candidate events as training labels, runs detection live during recording, and retrains as annotations accumulate. A detector trained on one mouse cohort (867 annotated seizures) achieved 93.6% precision in human review of 171 detections. Applied unchanged to a second cohort on the same acquisition system, its detections were mostly seizures (12 of 13 reviewed), but per-animal recall varied substantially (median 10%), with no convulsive reference seizure detected in four of 11 animals. Retraining with annotations from the second cohort raised convulsive recall from 15% to 64%, with at least one reference seizure detected in every animal. On recordings withheld from training, 75% of detections were seizures and 14 of 22 reference seizures were detected, whereas the source model and a model trained without these animals detected none. NED-Net supports evaluating and adapting detectors to local recordings. Competing Interest Statement The authors have declared no competing interest. Footnotes https://doi.org/10.5281/zenodo.23315438 https://github.com/EpilepsyCenter/NED-Net Funder Information Declared Swedish Research Council, https://ror.org/03zttf063 , 2022-01203 Stiftelsen Olle Engkvist Byggmästare, https://ror.org/00r9xw041 , 233-0293 Thorsten and Elsa Segerfalk Foundation, https://ror.org/008v8ay36 Åke Wibergs Stiftelse, https://ror.org/03htnqd27 Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Back to top Previous Next Posted October 21, 2026. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following NED-Net: an annotation-training loop for rodent EEG event detection Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share NED-Net: an annotation-training loop for rodent EEG event detection Mohd Yaqub MIR , Marco Ledri bioRxiv 2026.10.15.759362; doi: https://doi.org/10.64898/2026.10.15.759362 Share This Article: Copy Citation Tools NED-Net: an annotation-training loop for rodent EEG event detection Mohd Yaqub MIR , Marco Ledri bioRxiv 2026.10.15.759362; doi: https://doi.org/10.64898/2026.10.15.759362 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Areas All Articles Animal Behavior and Cognition (8164) Animal Behavior and Cognition (8165) Animal Behavior and Cognition (8165) Animal Behavior and Cognition (8165) Animal Behavior and Cognition (8165) Animal Behavior and Cognition (8165) Animal Behavior and Cognition (8165) Animal Behavior and Cognition Animal Behavior and Cognition (8165) Animal Behavior and Cognition Animal Behavior and Cognition (8165) Animal Behavior and Cognition Animal Behavior and Cognition Animal Behavior and Cognition (8165) Biochemistry (19027) Biochemistry (19029) Biochemistry (19029) Biochemistry Biochemistry Biochemistry Biochemistry Biochemistry Biochemistry (19029) Biochemistry (19029) Biochemistry (19029) Biochemistry Biochemistry (19029) Biochemistry (19029) Biochemistry (19029) Biochemistry (19029) Biochemistry Biochemistry (19029) Biochemistry Biochemistry (19029) Biochemistry Biochemistry (19030) Biochemistry (19030) Biochemistry (19030) Biochemistry (19030) Biochemistry (19030) Bioengineering (15171) Bioengineering (15172) Bioengineering Bioengineering (15176) Bioengineering (15176) Bioengineering (15172) Bioengineering (15176) Bioinformatics (45258) Bioinformatics (45254) Bioinformatics Bioinformatics (45257) Biophysics (22958) Biophysics (22961) Biophysics Biophysics (22958) Biophysics (22958) Biophysics Biophysics (22958) Biophysics (22958) Biophysics (22961) Cancer Biology (20081) Cancer Biology (20080) Cell Biology (27277) Clinical Trials (138) Clinical Trials (138) Clinical Trials (138) Developmental Biology (14134) Ecology (21378) Epidemiology (2067) Evolutionary Biology (25764) Genetics (16328) Genomics (23805) Immunology (19003) Microbiology (43137) Molecular Biology (18316) Neuroscience (94944) Paleontology (711) Pathology (3028) Pharmacology and Toxicology (5187) Physiology (8264) Plant Biology (16252) Scientific Communication and Education (2115) Synthetic Biology (4639) Systems Biology (10372) Zoology (2423)
## Related Clinical Research

- [Scalp EEG Signatures of Anterior Thalamic DBS: Reproducible Gamma Enhancement](https://medichelpline.com/clinical-feed/biorxiv-0-acute-scalp-eeg-signatures-of-anterior-thalamic-dbs-in-epilepsy-reproducible.md)
- [AI-Assisted Auto-Categorization Cuts Response Time for Patient Portal Messages in Neurology](https://medichelpline.com/clinical-feed/medrxiv-14-ai-tools-to-assist-with-management-of-patient-portal-messages-auto.md)
- [Intraoperative functional ultrasound distinguishes seizure-onset cortex and tracks responses to re](https://medichelpline.com/clinical-feed/medrxiv-13-interictal-functional-ultrasound-imaging-in-human-epilepsy-resection-and.md)
- [Humanized ATN1 Q112 Mouse Models DRPLA Epilepsy and Responds to Antisense Oligonucleotide Therapy](https://medichelpline.com/clinical-feed/biorxiv-3-a-humanized-mouse-model-of-drpla-recapitulates-epilepsy-and-demonstrates.md)
- [Safety of Stroboscopic Light Stimulation: Observational Evidence and Guidelines](https://medichelpline.com/clinical-feed/plos-one-23-stroboscopic-light-stimulation-safety-within-and-beyond-laboratory-settings.md)

## Navigation
- [← Back to Neurology Feed](https://medichelpline.com/clinical-feed/neurology.md)
- [← All Clinical Specialties](https://medichelpline.com/clinical-feed.md)
## Medical & Regulatory Disclaimer

> [!CAUTION]
> MedicHelpline content is structured for research, educational, and professional discovery purposes. It does not constitute individual medical advice, clinical diagnosis, or treatment recommendations.
> Always verify dosing, contraindications, and regulatory alerts against official product labeling and primary regulatory sources before clinical decision-making.