Screening Sleep Disordered Breathing with Noncontact Measurement in a Clinical Site
Authors: Matsuura Yutaka ,   Division of Health Science, Graduate School of Medicine, Osaka University, Jeong Hieyong , Yamada Kenji , Watabe Kenji , Yoshimoto Kayo , Ohno Yuko ,   Department of Robotics & Design for Innovative Healthcare, Graduate School of Medicine, Osaka University,   Department of Biodesign for Healthcare Innovation, Graduate School of Medicine, Osaka University,   Osaka University Hospital,   Department of Electrical and Information Engineering, Graduate School of Engineering, Osaka City University
Published: April 2017

[abstFig src='/00290002/06.jpg' width='300' text='Respiratory rate from simulator and Kinect' ]<span class=”bold”>Background and purpose:</span>It has been considered that sleep-disordered breathing disorders, such as sleep apnea syndrome (SAS), cause an increase in the risk of cardiovascular disease or traffic accident risk, and thus early detection of SAS is important. It has been also important for medical workers at clinical sites to quantitatively evaluate the respiratory condition of hospitalized patients who are asleep in a simple method. A noncontact-type system was proposed to monitor the respiratory condition of sleeping patients and minimized patient-related stress such that medical workers could use the system for SAS screening and perform a preliminary check prior to definite diagnosis.<span class=”bold”>Method:</span>The system included Microsoft Kinect™ for windows® (Kinect), a tripod, and a PC. A depth sensor of Kinect was used to measure movement in the thorax motion. Data obtained from periodic waveforms were divided with the intervals of 1 min, and the number of peaks was used to obtain the respiratory rate. Additionally, a frequency analysis was performed to calculate the respiratory frequency from a frequency at which the maximum amplitude was observed. In Experiment 1), a METI-man® PatientSimulator (CAE healthcare) (simulator) was used to study the respiratory rate and frequency calculated from the Kinect data by gradually changing the designated respiratory rate. In Experiment 2), the respiratory condition of four sleeping subjects was monitored to calculate their respiratory rate and frequencies. Furthermore, a video camera was used to confirm periodic waveforms and spectrum features of body movements during sleep.<span class=”bold”>Results:</span>In Experiment 1), the results indicated that both the respiratory rate and frequency corresponded to the designated respiratory rate in each time zone. In Experiment 2), the results indicated that the respiratory rate of examines 1, 2, 3, and 4 corresponded to 12.79±2.44 times/min (average ± standard deviation), 16.46±4.33 times/min, 28.24±2.79 times/min, and 13.05±2.64 times/min, respectively. The findings also indicated that the frequency of examines 1, 2, 3, and 4 corresponded to 0.20±0.04 Hz, 0.26±0.06 Hz, 0.45±0.12 Hz, and 0.22±0.06 Hz, respectively. The periodic waveforms and amplitude spectra were enhanced with respect to body movements although regular waveform data were obtained after the body movement occurred.<span class=”bold”>Discussions:</span>The results indicated that body movement and posture temporarily affected monitoring of the system. However, the findings also revealed that it was possible to calculate the respiratory rate and frequency, and thus it was considered that the system was useful for monitoring the respiration confirm with the non-contact or SAS screening of patients in clinical site.