Deep Learning-Based Event Counting for Apnea-Hypopnea Index Estimation Using Recursive Spiking Neural Networks.

Abstract

To develop a novel method for improved screening of sleep apnea in home environments, focusing on reliable estimation of the Apnea-Hypopnea Index (AHI) without the need for highly precise event localization. RSN-Count is introduced, a technique leveraging Spiking Neural Networks to directly count apneic events in recorded signals. This approach aims to reduce dependence on the exact time-based pinpointing of events, a potential source of variability in conventional analysis. RSN-Count demonstrates a superior ability to quantify apneic events (AHI MAE ) compared to established methods (AHI MAE ) on a dataset of whole-night audio and SpO recordings (N = 33). This is particularly valuable for accurate AHI estimation, even in the absence of highly precise event localization. RSN-Count offers a promising improvement in sleep apnea screening within home settings. Its focus on event quantification enhances AHI estimation accuracy. This method addresses limitations in current sleep apnea diagnostics, potentially increasing screening accuracy and accessibility while reducing dependence on costly and complex polysomnography.