Work

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

Year: 2025

Type: article

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.

Source: Sensors

DOI: 10.3390/s25165093

Authors: Milan Smetana, Ladislav Janousek

Institution: University of Žilina

Language: English

Topic: Phonocardiography and Auscultation Techniques

Subfield: Pulmonary and Respiratory Medicine

Field: Medicine

Domain: Health Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: Agentúra na Podporu Výskumu a Vývoja