Achieving Accurate Automatic Sleep Apnea/Hypopnea Syndrome Assessment Using Nasal Pressure Signal

Abstract

Automatic assessment of sleep apnea/ hypopnea syndrome (SAHS) based on fewer physiological signals is critical for the success of healthcare at home. However, previous studies that use such settings only achieve a lower assessment accuracy, causing fewer syndromes to be separated for effective diagnosis. This paper presents a 3-stage support vector machines (SVM)-based algorithm for SAHS assessment using a single-channel nasal pressure (NP) signal. In this work, NP signal is utilized for feature extraction. Amplitude features, as well as those extracted using discrete Fourier transform and discrete wavelet transform, are used for machine learning. A total of 58 sets of polysomnography recordings, each with approximately 7 h in duration, were analyzed. This work achieves a sensitivity of 95.7% and a positive predictive value of 90.9%, outperforming previous works using NP signal. Compared with prior studies using only SpO2 signal, this work still achieves better performance and supports more classification levels. Thanks to the low-complexity settings based only on the NP signal, the proposed approach provides a promising solution to SAHS assessment for remote healthcare.