UT-OSANet: A Multimodal Deep Learning model for Evaluating and Classifying Obstructive Sleep Apnea

TL;DR

UT-OSANet is a deep learning model that accurately detects and classifies sleep apnea events at a high resolution using multimodal data, improving diagnosis precision across various scenarios.

Contribution

The paper introduces UT-OSANet, a novel multimodal deep learning model capable of event-level diagnosis of OSA with flexible input modalities and cross-modal training strategies.

Findings

Achieved sensitivities up to 0.93 in detection tasks.

Macro F1 scores of 0.84 and 0.85 across different scenarios.

Effective in real-world clinical and research settings.

Abstract

Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder that is associated with increased risks of cardiovascular morbidity and all-cause mortality. While existing diagnostic approaches can roughly classify OSA severity or detect isolated respiratory events, they lack the precision and comprehensiveness required for high resolution, event level diagnosis. Here, we present UT OSANet, a deep learning based model designed as a event level, multi scenario diagnostic tool for OSA. This model facilitates detailed identification of events associated with OSA, including apnea, hypopnea, oxygen desaturation, and arousal. Moreover, the model employs flexibly adjustable input modalities such as electroencephalography (EEG), airflow, and SpO 2. It utilizes a random masked modality combination training strategy, allowing it to comprehend cross-modal relationships while sustaining consistent performance across varying modality conditions. This model was trained and evaluated utilizing 9,021 polysomnography (PSG) recordings from five independent datasets. achieving sensitivities up to 0.93 and macro F1 scores of 0.84, 0.85 across home, clinical, and research scenarios. This model serves as an event-level, multi-scenario diagnostic instrument for real-world applications of OSA, while also establishing itself as a means to deepen the mechanistic comprehension of respiratory processes in sleep disorders and their extensive health implications.