OpenSpindleNet: An open-source deep learning network for reliable sleep spindle detection in scalp and intracranial EEG
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21460%2F25%3A00388312" target="_blank" >RIV/68407700:21460/25:00388312 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21730/25:00388312
Result on the web
<a href="https://doi.org/10.1016/j.compbiomed.2025.110854" target="_blank" >https://doi.org/10.1016/j.compbiomed.2025.110854</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.compbiomed.2025.110854" target="_blank" >10.1016/j.compbiomed.2025.110854</a>
Alternative languages
Result language
angličtina
Original language name
OpenSpindleNet: An open-source deep learning network for reliable sleep spindle detection in scalp and intracranial EEG
Original language description
Sleep spindles, an oscillatory brain activity occurring during light non-rapid eye movement (NREM) sleep, are important for memory consolidation and cognitive functions. Accurate detection is important for understanding the role of spindles in sleep state physiology and brain health and for better understanding sleep and neurological disorders. However, manual spindle labeling of electroencephalography (EEG) data is time-consuming and impractical for most clinical and research settings and intracranial EEG (iEEG) presents additional challenges for spindle identification due to its unique signal characteristics and recording environment. This study introduces a novel, precise, and automatic spindle detection method for iEEG using a dual-head architecture to enhance performance, robustness, and ease of use. Our approach achieves a detection F1 score of 0.67 on a challenging iEEG dataset and 0.69 on the publicly available scalp EEG DREAMS dataset. Compared to existing methods such as SUMO, A7, and YASA, our model demonstrates superior performance in detecting, segmenting, and characterizing sleep spindles. This model contributes to open science and advances automated sleep spindle classification in iEEG. This will advance the development of more precise diagnostic and research tools and facilitate a deeper understanding of the role of sleep spindles in cognitive processes and neurological health. Copyright 2025 Elsevier Ltd. All rights reserved.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
R - Projekt Ramcoveho programu EK
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Computers in Biology and Medicine
ISSN
0010-4825
e-ISSN
1879-0534
Volume of the periodical
197
Issue of the periodical within the volume
Pt A
Country of publishing house
GB - UNITED KINGDOM
Number of pages
17
Pages from-to
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UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-105016544239