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WaveSleepNet: An interpretable deep convolutional neural network for the continuous classification of mouse sleep and wake

Journal of Neuroscience Methods(2021)10.1016/j.jneumeth.2021.109224Source: DataRank Database

WaveSleepNet: An interpretable deep convolutional neural network for the continuous classification of mouse sleep and wake is a research paper published in Journal of Neuroscience Methods (2021). On theSindex it has a DataRank of 0.610. It has been cited 10 times, with 9 citing works in its 1-hop citation network.

N/A

0.610DataRank · unranked

Scored on demand from live citation data

Open Access10 citations · 9 citing works in its network

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

Pipeline:MetadataData-paper checkEnrichmentCitation networkScoring

Enrichment:Funding (4 grants)Impact (FWCI 0.84)OA: greenTopics (28)IDs (PubMed, OpenAlex)

FAIR Checklist

Context only (not used in score)

Findable (1/2)

Has DOI

Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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DataRank Breakdown

Base Score 59%Citation Network 41%

Base Score Contribution

0.360

From this paper's citation signal

Citation Network Contribution

0.250

From 6 citing papers with measurable signal

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Top 5 citers driving the network score

Ranked by each citer's contribution to N(p) — log1p(C_q) divided by its reference count — out of 9 citers.

Optimization of real-time analysis of sleep-wake cycle in mice
MethodsX20225 citations17 referencesContributes 0.105
The Research of Chronic Gastritis Diagnosis with Electronic Noses
Journal of Sensors202110 citations38 referencesContributes 0.063
Sleep-Deep-Learner is taught sleep–wake scoring by the end-user to complete each record in their style
SLEEP Advances20244 citations36 referencesContributes 0.045
Long Short-Term Memory-based Models for Sleep Quality Prediction from Wearable Device Time Series Data
Procedia Computer Science20232 citations28 referencesContributes 0.039
Sleep-Deep-Net learns sleep wake scoring from the end-user and completes each record in their style
bioRxiv (Cold Spring Harbor Laboratory)20231 citations33 referencesContributes 0.021

Authors (5)

David M. Rapoport ORCID,Ankit Parekh ORCID,Indu Ayappa ORCID,Andrew W. Varga ORCID,Korey Kam ORCID

Live enrichment

PubMed:34052291 OpenAlex:https://openalex.org/W3165740611

NIH

Grant: R01 AG056682

NIH

Grant: R01 AG066870

NIH

Grant: R21 AG059179

Alzheimer's Association

Grant: 2018-AARG-589632

FWCI

0.84

Citation Percentile

0.7%

Citation Trend

2021

2022

2023

2024

2025

2026

greenLicense: http://www.elsevier.com/open-access/userlicense/1.0/

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/8457529

publisherhttps://api.elsevier.com/content/article/PII:S016502702100159X?httpAccept=text/xml

publisherhttps://api.elsevier.com/content/article/PII:S016502702100159X?httpAccept=text/plain

journalhttps://doi.org/10.1016/j.jneumeth.2021.109224

Fields of Study

Sleep and Wakefulness ResearchEEG and Brain-Computer InterfacesSleep and Work-Related Fatigue

MeSH Terms

AnimalsElectroencephalographyHumansSleepSleep StagesSleep, REMNeural Networks, ComputerMice

Keywords

Convolutional neural networkComputer scienceArtificial intelligenceNon-rapid eye movement sleepEpoch (astronomy)Sleep StagesElectroencephalographySleep (system call)PolysomnographyClassifier (UML)Pattern recognition (psychology)Fragmentation (computing)Machine learningEye movementPsychologyNeuroscienceComputer vision