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

Abstract

Highlights: What are the main findings? Robust and cost-effective measurement set-up: The system is designed to ensure re-liability in sound analysis while being cost-effective for long-term use. Crucial for doctors: A critical knowledge base is essential for healthcare professionals to make informed decisions and implement effective therapies for pediatric patients. What is the implication of the main finding? High accuracy of achieved results: Using time-domain analysis, we demonstrated a detailed representation of sound architecture. The results ensure reliability in the categorization process. The proposed LSTM neural network, trained on a dataset of 1500 sounds per category, features two layers. The deep neural network achieved 91.16% overall, with individual channel accuracy reaching 93.35%. 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.