Abstract

PurposeSleep apnea and hypopnea syndrome (SAHS) seriously affects sleep quality. In recent years, much research has focused on the detection of SAHS using various physiological signals and algorithms. The purpose of this study is to find an efficient model for detection of apnea-hypopnea events based on nasal flow and SpO2 signals.MethodsA 60-s detector and a 10-s detector were cascaded for precise detection of apnea-hypopnea (AH) events. Random forests were adopted for classification of data segments based on morphological features extracted from nasal flow and arterial blood oxygen saturation (SpO2). Then the segments’ classification results were fed into an event detector to locate the start and end time of every AH event and predict the AH index (AHI).ResultsA retrospective study of 24 subjects’ polysomnography recordings was conducted. According to segment analysis, the cascading detection model reached an accuracy of 88.3%. While Pearson’s correlation coefficient between estimated AHI and reference AHI was 0.99, in the diagnosis of SAHS severity, the proposed method exhibited a performance with Cohen’s kappa coefficient of 0.76.ConclusionsThe cascading detection model is able to detect AH events and provide an estimate of AHI. The results indicate that it has the potential to be a useful tool for SAHS diagnosis.

Highlights

  • Sleep apnea and hypopnea syndrome (SAHS) is a prevalent sleep breathing disorder in middle-aged people

  • We analyzed its performance with respect to two aspects: segments and AH index (AHI)

  • We proposed a cascading detection model that could predict AHI based on AH event detection

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Summary

Introduction

Sleep apnea and hypopnea syndrome (SAHS) is a prevalent sleep breathing disorder in middle-aged people. The gold standard for diagnosis of SAHS is to perform polysomnography (PSG) in a laboratory. PSG requires patients to sleep with many sensors for at least one night; the scoring of apneahypopnea (AH) events can take a long time. Many researchers hope to simplify or replace PSG by using a limited number of physiological signals. Electrocardiogram (ECG) was first studied for this purpose. McNames et al [1] found that heart rate, S-pulse amplitude, and pulse energy were correlated with SAHS. Bsoul et al [2] cut the ECG into 60-s segments and used

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