OSCR

SleepPACNet: new convolutional neural network considering phase-amplitude coupling for automatic sleep stage classification using single-channel electroencephalogram.

Overview

Authors: Taegyeong Lee1, Hyerin Nam2, JangJay Sohn1, Chang-Hwan Im1,2,3
ORCID iDs: JangJay Sohn
  1. Department of Electronic Engineering, Hanyang University,222 Wangsimni-ro, Seongdong- gu, Seoul, 04763 Republic of Korea
  2. Department of Artificial Intelligence, Hanyang University,222 Wangsimni-ro, Seongdong-gu, Seoul, 04763 Republic of Korea
  3. Department of Biomedical Engineering, Hanyang University,222 Wangsimni-ro, Seongdong-gu, Seoul, 04763 Republic of Korea
Institutions: Hanyang University (South Korea)
Journal: Scientific reports, volume 16, issue 1, article 17317
Dates: received 7 November 2025; accepted 10 April 2026; published online 13 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-48881-w · PMID 41974873 · PMCID PMC13234012 · OpenAlex W7154144498
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: EEG (modality), cellular / molecular (subfield)
Methods: Spectral & time-frequency, Statistics, Machine learning, Preprocessing, Connectivity, Physiology & signal measures
Keywords: Automatic sleep stage classification (ASSC), Electroencephalography (EEG), Phase-amplitude coupling, Hilbert transform, Convolutional neural network (CNN), Computational biology and bioinformatics, Engineering, Neuroscience
Topic: EEG and Brain-Computer Interfaces (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: National Research Foundation of Korea (RS-2024-00397673); KYUNG NAM PHARM. CO., LTD
Citations: not cited yet (Europe PMC); 65 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Code

The paper links to its data, not to its authors' code: see the Data section.

Tracing map

A tracing map links a paper to the code its authors published: this paper has none, so it has no map.

Data

Datasets cited

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41598-026-48881-w.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 8 keywords, 2 funders, 55 references.

Cite

This paper

Lee, T., Nam, H., Sohn, J., & Im, C.-H. (2026). SleepPACNet: new convolutional neural network considering phase-amplitude coupling for automatic sleep stage classification using single-channel electroencephalogram. Scientific reports, 16(1), 17317. https://doi.org/10.1038/s41598-026-48881-w

BibTeX

@article{lee2026sleeppacnet,
author = {Lee, Taegyeong and Nam, Hyerin and Sohn, JangJay and Im, Chang-Hwan},
title = {{SleepPACNet: new convolutional neural network considering phase-amplitude coupling for automatic sleep stage classification using single-channel electroencephalogram}},
journal = {Scientific reports},
year = {2026},
month = apr,
volume = {16},
number = {1},
pages = {17317},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-48881-w},
url = {https://doi.org/10.1038/s41598-026-48881-w},
pmid = {41974873},
pmcid = {PMC13234012}
}

RIS

TY - JOUR
AU - Lee, Taegyeong
AU - Nam, Hyerin
AU - Sohn, JangJay
AU - Im, Chang-Hwan
TI - SleepPACNet: new convolutional neural network considering phase-amplitude coupling for automatic sleep stage classification using single-channel electroencephalogram
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/04/13
VL - 16
IS - 1
SP - 17317
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-48881-w
UR - https://doi.org/10.1038/s41598-026-48881-w
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41598-026-48881-w",
"type": "article-journal",
"title": "SleepPACNet: new convolutional neural network considering phase-amplitude coupling for automatic sleep stage classification using single-channel electroencephalogram",
"container-title": "Scientific reports",
"author": [
{
"family": "Lee",
"given": "Taegyeong"
},
{
"family": "Nam",
"given": "Hyerin"
},
{
"family": "Sohn",
"given": "JangJay"
},
{
"family": "Im",
"given": "Chang-Hwan"
}
],
"container-title-short": "Sci Rep",
"volume": "16",
"issue": "1",
"page": "17317",
"DOI": "10.1038/s41598-026-48881-w",
"PMID": "41974873",
"PMCID": "PMC13234012",
"ISSN": "2045-2322",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41598-026-48881-w",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
13
]
]
}
}

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1038/s41598-026-57532-z
Predictability of sleep slow oscillation emergence and spatial extent from pre-onset neural dynamics.
Journal: Scientific reports
In common: EEG, 4 references
[2] doi:10.1371/journal.pone.0353930 [code]
MMFNet: A multi-branch multi-scale framework with adaptive sparse self-attention and cross-modal fusion for sleep stage assessment.
Journal: PloS one
In common: EEG, 3 references
[3] doi:10.1371/journal.pone.0351872 [code]
Decoding visual object recognition from EEG signals.
Journal: PloS one
In common: EEG, 3 references
[4] doi:10.1098/rstb.2024.0466
At-home limbic-targeted transcranial electrical stimulation during sleep: a feasibility study in healthy adults.
Journal: Philosophical transactions of the Royal Society of London. Series B, Biological sciences
In common: EEG, 2 references
[5] doi:10.3390/diagnostics16162609
Automated Multimodal Sleep Staging Using DWT-Based Wavelet Decomposition and Explainable Machine Learning with Signal Sculpting Topographies.
Journal: Diagnostics (Basel, Switzerland)
In common: EEG, 2 references
[6] doi:10.1093/sleepadvances/zpag044 [code]
REST-a deep learning tool for automated mouse sleep stage classification.
Journal: Sleep advances : a journal of the Sleep Research Society
In common: EEG, 2 references
[7] doi:10.1371/journal.pone.0346294
Neural network architectures and normalization techniques for automated sleep stage classification using rodent EEG and EMG signals.
Journal: PloS one
In common: EEG, 2 references
[8] doi:10.1093/braincomms/fcag204 [code]
Altered rhythmic and arrhythmic electroencephalographic activity during non-rapid eye movement sleep in amnestic mild cognitive impairment.
Journal: Brain communications
In common: EEG, 2 references
[9] doi:10.1038/s41467-026-75345-6 [code]
Hippocampal ripples initiate cortical dimensionality expansion for memory retrieval.
Journal: Nature communications
In common: EEG, 2 references
[10] doi:10.1073/pnas.2516293123
Distinct laminar origins of sensory-evoked high-gamma and low-frequency ECoG signals revealed by optogenetics.
Journal: Proceedings of the National Academy of Sciences of the United States of America
In common: EEG, 2 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.