Evidence map›Paper›PMID 40871956›Full record

ArticleSensors (Basel, Switzerland)2025

High-Precision Contactless Stereo Acoustic Monitoring in Polysomnographic Studies of Children.

Milan Smetana, Ladislav Janousek

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Milan SmetanaDepartment of Electromagnetic and Biomedical Engineering, Faculty of Electrical Engineering and Information Technology, University of Zilina, Univerzitna 8215/1, 010 26 Zilina, Slovakia.
Ladislav JanousekDepartment of Electromagnetic and Biomedical Engineering, Faculty of Electrical Engineering and Information Technology, University of Zilina, Univerzitna 8215/1, 010 26 Zilina, Slovakia.ORCID 0000-0002-4823-8910

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper focuses on designing a robust stereophonic measurement set-up for sound sleep recording. The system is employed throughout the night during polysomnographic examinations of children in a pediatric sleep laboratory at a university hospital. Deep learning methods were used to classify the sounds in the recordings into four categories (snoring, breathing, silence, and other sounds). Specifically, a recurrent neural network with two long short-term memory layers was employed for classification. The network was trained using a dataset containing 1500 sounds from each category. The deep neural network achieved an accuracy of 91.16%. We developed an innovative algorithm for sound classification, which was optimized for accuracy. The results were presented in a detailed report, which included graphical representations and sound categorization throughout the night.

Indexed as

AcousticsPolysomnographyAlgorithmsChildDeep LearningFemaleHumansMaleMonitoring, PhysiologicNeural Networks, ComputerSleepSnoringSoundclassification of sleep soundsdeep learningneural networkspolysomnographysleep soundsstereophony

Identifiers

PMID40871956
PMCPMC12390021

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.