A wireless, real-time respiratory effort and body position monitoring system for sleep. (August 2020)

• Record Type: Journal Article
• Authors: Hernandez, Joel Ezequiel; Cretu, Edmond
• Is Part Of: Biomedical signal processing and control. Volume 61(2020)
• Publication Date: 2020-08
• Subjects: Sleep monitoring -- Respiratory effort -- Body position -- Data fusion -- Inertial measurement units; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2020.102023
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: The number of people suffering from a sleep disorder is growing considerably, with obstructive sleep apnea syndrome (OSAS) being the most common one. Unfortunately, it is estimated that at least 75% of OSAS cases are currently undiagnosed and without treatment, mainly due to the lack of availability and high costs of the polysomnography (PSG) test required. OSAS is known to increase the risk for many severe health complications such as hypertension and neurodegenerative conditions, to mention a few, making early diagnosis important. The monitoring of respiratory effort (RE) and body position (BP) during sleep is essential for OSAS detection. Compared to existing commercial systems for respiratory effort monitoring using piezoelectric sensors wired to a central processing system, we present an embedded system solution that is wireless, runs standalone for up to 12 h, and implements on board an extended Kalman filter (EKF) for performing data fusion. An inertial measurement unit (3D acceleration, angular rate, and orientation sensors) is used for real-time measurement of both the respiratory effort and the body position. The experimental tests, when we compare the collected data with a reference signal, acquired by a commercial belt, indicate a general average Pearson correlation coefficient of ρ = 0.963 with results ranging from 0.928 to 0.981, improving over previous works. Low-cost technologies, such as the one presented here, can help to reduce the number of undiagnosed cases of OSAS, and sleep disorders in general, and improve the health of those patients.