Werthen Brabants, LorinLorinWerthen BrabantsCastillo-Escario, YolandaYolandaCastillo-EscarioGroenendaal, WillemijnWillemijnGroenendaalJane, RaimonRaimonJaneDhaene, TomTomDhaeneDeschrijver, DirkDirkDeschrijver2025-03-312025-03-312025-APR0018-9294WOS:001449682300028https://imec-publications.be/handle/20.500.12860/45465Objective: 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.Journal article10.1109/TBME.2024.3498097WOS:001449682300028SLEEP-APNEAPREDICTIONSOUNDMEDLINE:40030371