Automated Sleep apnea detection using optimal duration-frequency concentrated wavelet-based features of pulse oximetry signals

Automated Sleep apnea detection using optimal duration-frequency concentrated wavelet-based features of pulse oximetry signals

Sleep apnea is a potential sleep disorder, which deteriorates the quality of sleep. It is characterized by the obstruction in nasal airflow, which results in a low concentration of oxygen in the blood. Though polysomnography (PSG) is considered as a gold standard for diagnosing sleep apnea, it is arduous, demanding, expensive and inconvenient to the patients. This study presents an effective, efficient and sustainable sleep apnea automated detection system using pulse oximetry signals (SpO2), which indicate the percentage of oxygen content in the blood. The conventional methods, which employ PSG recordings are computationally intensive and costly. Nowadays, the focus is on non-invasive and portable devices for higher convenience and cost-effective diagnosis. In this work, we have used optimal duration-bandwidth concentrated wavelet transform to decompose the SpO2 signals into various sub-bands (SBs). The Shannon entropy features are extracted from various SBs coefficients. These features are then fed to various supervised machine learning algorithms, including decision trees and ensemble algorithms for automated detection of sleep apnea. The proposed model has attained the highest accuracy of 95.97%, and area under the receivers operating characteristics curve (AUC) of 0.98 for optimal wavelet-based Shannon entropy features when an ensemble boosting technique called random under-sampling boosting (RUSBoost) is employed with ten-fold cross-validation strategy. Thus, the proposed model is portable, economical, and accurate which can be used even at homes.

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