A New Multi-feature Classification Scheme for Normal and Abnormal Respiratory Sounds Discrimination

During sleep, breathing-related sleep disorders (BSD) are very probable to cause distortions on human health and even be life-threatening. Among the different types of BSD, apnea accounts for one of the most common. Many detection algorithms have been proposed for spotting and classifying apneas, using one feature or being designed for binary classification. Also, many proposed clinical setups for respiratory data acquisition are invasive, making the application to patients a non-trial task. In this study, we aim to propose an easy-to-apply and patient-friendly clinical setup with a BSD detection that utilizes a multi-feature classification scheme for binary (apnea, healthy), as well as multiple classes (healthy, central, mixed, and obstructive apneas and hypopneas). Our clinical setup includes a high-resolution microphone attached to the bed at a very close distance to the patient. Our multi-feature approach contains spectral, statistical, and symbolic-based characteristics of respiratory signals of five patients admitted for a first BSD diagnosis and assesses the performance of different classification algorithms iteratively. The results show a high classification performance (> 98% for binary and > 84% for multi-class classification) for either classification scheme. A robust classification scheme is thus proposed, utilizing the entire content of the recorded respiratory signal. Such a classification scheme leads to a promising result towards the design of portable devices with multi-features for real-time detection of BSD.