An Intelligent Classification Diagnosis based on Blood Oxygen Saturation Signals for Medical Data Supply Chain Including COVID-19 in Industry 5.0

ABSTRACT

Obstructive sleep apnea-hypopnea syndrome (OSAHS) has been gradually valued due to its high prevalence, high risk, and high mortality. This article is to find an alternative to the polysomnography (PSG) OSAHS diagnosis method and assesses the subject's degree of illness considering the supply chain and Industry 5.0 requirement, efficiently, accurately and easily. The blood oxygen saturation (SpO2) signal is used to count the number of apnea or hypoventilation events. It extracts 35-dimensional features based on the time domain to enhance the process resilience, including approximate entropy, Centralized Trend Measurement (CTM), and LZ complexity for the diagnosis process in supply chains. This article summarizes the Oxygen Desaturation Index (ODI) characteristics. The feature selection process is reduced from 35 to 7 dimensions and benefits the implementation in the practical supply chains in industry 5.0. A 92% accuracy rate is reached in assessing the prevalence of OSAHS, satisfying the industrial deployment. IEEE