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

Abstract

Objective: 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. Methods: 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. Results: RSN-Count demonstrates a superior ability to quantify apneic events (AHI MAE 6.17±2.21) compared to established methods (AHI MAE 8.52±3.20) on a dataset of whole-night audio and SpO2 recordings (N = 33). This is particularly valuable for accurate AHI estimation, even in the absence of highly precise event localization. Conclusion: RSN-Count offers a promising improvement in sleep apnea screening within home settings. Its focus on event quantification enhances AHI estimation accuracy. Significance: This method addresses limitations in current sleep apnea diagnostics, potentially increasing screening accuracy and accessibility while reducing dependence on costly and complex polysomnography.