Sleep Apnea Detection with Polysomnography and Depth Sensors

Abstract

This paper is devoted to proving two goals, to show that various depth sensors can be used to record breathing rate with the same accuracy as contact sensors used in polysomnography (PSG), in addition to proving that breathing signals from depth sensors have the same sensitivity to breathing changes as in PSG records. The breathing signal from depth sensors can be used for classification of sleep apnea events with the same success rate as with PSG data. The recent development of computational technologies has led to a big leap in the usability of range imaging sensors. New depth sensors are smaller, have a higher sampling rate, with better resolution, and have bigger precision. They are widely used for computer vision in robotics, but they can be used as non-contact and non-invasive systems for monitoring breathing and its features. The breathing rate can be easily represented as the frequency of a recorded signal. All tested depth sensors (MS Kinect v2, RealSense SR300, R200, D415 and D435) are capable of recording depth data with enough precision in depth sensing and sampling frequency in time (20–35 frames per second (FPS)) to capture breathing rate. The spectral analysis shows a breathing rate between 0.2 Hz and 0.33 Hz, which corresponds to the breathing rate of an adult person during sleep. To test the quality of breathing signal processed by the proposed workflow, a neural network classifier (simple competitive NN) was trained on a set of 57 whole night polysomnographic records with a classification of sleep apneas by a sleep specialist. The resulting classifier can mark all apnea events with 100% accuracy when compared to the classification of a sleep specialist, which is useful to estimate the number of events per hour. When compared to the classification of polysomnographic breathing signal segments by a sleep specialist, which is used for calculating length of the event, the classifier has an F 1 score of 92.2% Accuracy of 96.8% (sensitivity 89.1% and specificity 98.8%). The classifier also proves successful when tested on breathing signals from MS Kinect v2 and RealSense R200 with simulated sleep apnea events. The whole process can be fully automatic after implementation of automatic chest area segmentation of depth data.

Advancements in Methods and Camera-Based Sensors for the Quantification of Respiration

“…The characterization of chest wall motion has been addressed in many methodological works, which presented interesting results, including for the general (e.g., motion quantification) and specific levels (e.g., illness detection/severity assessment and scoring). This can be grouped as follows: [ 57 , 61 , 62 , 64 , 68 , 72 , 117 , 120 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , 137 , 138 , 139 , 140 , 141 , 142 , 143 , 144 , 145 , 146 , 147 , 148 , 149 ] (2010–2015), [ 3 , 10 , 47 , 48 , 49 , 53 , 54 , 95 , 97 , 99 , 101 , 105 , 111 , 122 , 150 , 151 , 152 , 153 , 154 , 155 , 156 , 157 , 158 ...…”

“…Schätz et al used a variety RGB-D sensor, including the D415, and D435 ASV sensors to capture the breathing rate of a sleeping person [ 111 ]. Authors showed that depth sensors could be used to record breathing rate with the same accuracy as contact sensors used in polysomnography (PSG).…”

“…This topic has grown rapidly in the last decade. Research approaches were moving towards investigation of many types of imaging data such as color [ 10 , 113 ], depth [ 91 , 111 , 112 ], and infrared [ 107 , 108 , 114 , 115 , 159 , 247 ] images. This trend resulted from the emergence of new affordable acquisition devices, such as, Kinect depth sensor [ 32 , 46 ], Orbbec Astra mobile 3D cameras [ 48 , 240 ], RealSense technology [ 105 ], and FLIR thermic cameras [ 48 , 239 ].…”

The Importance of Respiratory Rate Monitoring: From Healthcare to Sport and Exercise

“…To this end, sleep apnea can be detected with a mobile phone built-in microphone [81,82]. Other available techniques for apnea monitoring include the use of camera sensors for the recording of surveillance videos that can be post-processed to retrieve apnea episodes [83,84]. Besides, techniques based on instrumented items (e.g., sleep mats) have also been designed and tested, but further research is needed to improve their sensitivity to sleep apnea detection [85].…”

Fiber-Optic Based Smart Textiles for Real-Time Monitoring of Breathing Rate

“…The authors came to the conclusion that body-conducted sensor performs better in four different types of the experiments. Schatz et al [ 3 ] studied the application of five different types of depth sensors for breathing rate monitoring and usage of this data for the sleep apnea identification. All of the sensors output were compared with the reference sensor and two of the five sensors have been found appropriate for sleep apnea determination.…”