Automated sleep apnea detection in pregnant women using wavelet-based features

Abstract

Sleep apnea is a severe sleep disorder that degrades the quality of sleep and causes patients to feel restless and fatigued even after a full night's sleep. Sleep-disordered breathing (SDB) research has grown its importance in recent decades, owing to patients' substantial health effects and poor quality of life. Obstructive sleep apnea (OSA), the most prevalent kind of sleep disorder, that is caused due to a partial blockage in the nasal airway, resulting in low oxygen levels in the patient's blood. The drop in oxygen level may cause high blood pressure, which can lead to major cardiac issues. Further, OSA is also a risk for pregnancy complications in first-time pregnant women (nulliparous pregnancy), as emerging research and studies suggest a strong relationship between sleep-disordered breathing patients and critical pregnancy outcomes like preeclampsia, preterm birth, gestational diabetes, and intrauterine growth retardation.Polysomnography (PSG) recordings with multiple-channel various modalities signals are often used to detect sleep problems. This study aims to detect sleep apnea in nulliparous pregnant women using a simplified PSG-based system that employed only airflow signals. We used the ‘nulliparous pregnancy outcomes study monitoring mothers-to-be (nuMOM2b)’ database, containing 3012 women subjects for sleep-disordered breathing (SDB) related sub-studies. To the best of our knowledge, this is the first computer-aided diagnostic tool developed to automatically identify sleep apnea in pregnant women. In this work on the detection of OSA, we proposed the use of a single-channel airflow (AF) signal in combination with an orthogonal wavelet filter bank. The L1 − norm features are extracted from wavelet coefficients. The selected features are then fed into various machine learning classifiers to automatically detect apnea. The developed model obtained a highest accuracy of 83.9% with an F1 score of 0.91 using the RUSBoosted tree classifier with ten-fold cross-validation (CV) strategy. The system proposed can be used in a home-based compact clinical setup to detect apnea in pregnant women.