Abstract
The present paper proposes the design of a sleep monitoring platform. It consists of an entire sleep monitoring system based on a smart glove sensor called UpNEA worn during the night for signals acquisition, a mobile application, and a remote server called AeneA for cloud computing. UpNEA acquires a 3-axis accelerometer signal, a photoplethysmography (PPG), and a peripheral oxygen saturation (SpO) signal from the index finger. Overnight recordings are sent from the hardware to a mobile application and then transferred to AeneA. After cloud computing, the results are shown in a web application, accessible for the user and the clinician. The AeneA sleep monitoring activity performs different tasks: sleep stages classification and oxygen desaturation assessment; heart rate and respiration rate estimation; tachycardia, bradycardia, atrial fibrillation, and premature ventricular contraction detection; and apnea and hypopnea identification and classification. The PPG breathing rate estimation algorithm showed an absolute median error of 0.5 breaths per minute for the 32 s window and 0.2 for the 64 s window. The apnea and hypopnea detection algorithm showed an accuracy (Acc) of 75.1%, by windowing the PPG in one-minute segments. The classification task revealed 92.6% Acc in separating central from obstructive apnea, 83.7% in separating central apnea from central hypopnea and 82.7% in separating obstructive apnea from obstructive hypopnea. The novelty of the integrated algorithms and the top-notch cloud computing products deployed, encourage the production of the proposed solution for home sleep monitoring.
Highlights
Sleep is a physiological activity that influences daily life in different ways, and its measurement allows one to evaluate how well a person is sleeping
The efficacy of the data compression and encoding algorithms will be presented, the performances of the breathing rate estimation methods followed by those obtained from the apnea and hypopnea detection and classification analysis
At last, during the testing phase, it has not been possible to evaluate the sleeping stage classifier based on movement detection; the authors decided to integrate it into the platform as an indicative prediction index
Summary
Sleep is a physiological activity that influences daily life in different ways, and its measurement allows one to evaluate how well a person is sleeping. According to the U.S.A. National Sleep Foundation, sleep quality assesses if the sleep is restful and restorative [1]. National Sleep Foundation, sleep quality assesses if the sleep is restful and restorative [1] It is fundamental for the health and well-being of people at all stages of life [2]. The U.S.A. National Institutes of Health (NIH) recognizes that chronic sleep deficiency and circadian disruption are emerging characteristics of modern urban lifestyles and are associated with public safety and increased disease risk, through multiple complex pathways in all age groups [3]. Sleep disordered breathing is associated with cardiovascular and metabolic risk factors, attention-related behavioral problems, and poor academic performance [4].
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