An IoMT cloud-based real time sleep apnea detection scheme by using the SpO2 estimation supported by heart rate variability

Obstructive sleep apnea refers to a highly rampant sleep-related breathing disorder. The gold standard examination for diagnosis is polysomnography. Even though it provides highly accurate results, this multi-parametric test is time consuming and expensive. It also does not align with the new trend in health care, where focus is shifted to wellness and prevention. One possible way to address this problem is home health care, through the use of minimal invasive devices, higher accessibility, and provision of low cost diagnosis. To manage this, an automated and portable sleep apnea detector was formulated and assessed. The device utilizes one SpO2 sensor for estimating the heart rate and the oxygen blood level as well. The basis of the proposed analysis method is the connection between heart rate variability and oxygen saturation with d apnea events. The measured signals were then transferred to a cloud-based system architecture to diagnose and warn the remote patients. This solution was used to process the data and display it on both the mobile phone and personal computer. Testing of the proposed algorithms was done using the St. Vincents University Hospital/University College Dublin sleep apnea database. Apart from this database, the researchers utilized data gathered from 10 apnea patient volunteers. The performance of the proposed scheme algorithm achieved an average accuracy, specificity, and sensitivity of 98.54, 98.95%, and 97.05%, respectively. (C) 2018 Elsevier B.V. All rights reserved.