Multispectral Video Fusion for Non-Contact Monitoring of Respiratory Rate and Apnea

Abstract

Continuous monitoring of respiratory activity is desirable in many clinical applications to detect respiratory events. Non-contact monitoring of respiration can be achieved with near-and far-infrared spectrum cameras. However, current technologies are not sufficiently robust to be used in clinical applications. For example, they fail to estimate an accurate respiratory rate (RR) during apnea. We present a novel algorithm based on multispectral data fusion that aims at estimating RR also during apnea. The algorithm independently addresses the RR estimation and apnea detection tasks. Respiratory information is extracted from multiple sources and fed into an RR estimator and an apnea detector whose results are fused into a final respiratory activity estimation. We evaluated the system retrospectively using data from 30 healthy adults who performed diverse controlled breathing tasks while lying supine in a dark room and reproduced central and obstructive apneic events. Combining multiple respiratory information from multispectral cameras improved the root mean square error (RMSE) accuracy of the RR estimation from up to 4.64 monospectral data down to 1.60 breaths/min. The median F1 scores for classifying obstructive (0.75 to 0.86) and central apnea (0.75 to 0.93) also improved. Furthermore, the independent consideration of apnea detection led to a more robust system (RMSE of 4.44 vs. 7.96 breaths/min). Our findings may represent a step towards the use of cameras for vital sign monitoring in medical applications.

Estimation of Motion and Respiratory Characteristics during the Meditation Practice Based on Video Analysis

“…The authors of paper [ 26 ] introduce an algorithm to estimate the RR during apnea based on multi-spectral data fusion. The respiratory information is extracted from multiple sources (far-infrared and near-infrared cameras).…”

A Multi-scale Residual Convolutional Neural Network for Sleep Staging Based on Single Channel Electroencephalography Signal

“…Rahman [15] used EOG signals to obtain up to 73% accuracy of sleep staging, but the classification effect of N1 stage still needs to be improved. In recent years, non-invasive or noncontact measurement of cardiac, respiratory and body movement signals has gradually gained the favor of researchers [16][17][18][19][20]. Ballistocardiograph (BCG) measurements based on pressure-sensitive sheets or bedmounted measurements have been used for sleep analysis and preliminary screening for diseases [16][17][18].…”

“…There are two main types of current research on sleep monitoring and staging methods, one is to simplify and optimize the signal acquisition channel, the other is to explore change rules of other physiological signals during sleep [11][12][13][14][15][16][17][18][19][20]. Wearable measurements of physiological signals such as ECG, EOG and respiration, as well as body movements and other behaviors, offer the potential of low cost and easy operation for longterm dynamic sleep monitoring [12][13][14][15].…”

“…Ballistocardiograph (BCG) measurements based on pressure-sensitive sheets or bedmounted measurements have been used for sleep analysis and preliminary screening for diseases [16][17][18]. Methods such as video monitoring or microwave radar were also used in the detection of cardiac, respiratory and body movement of patients [19][20]. Of course, their performances mainly depends on the quality of signal acquisition.…”

Examination of Potential of Thermopile-Based Contactless Respiratory Gating

“…Thermal-based respiration monitoring has been proposed and demonstrated on both high and low resolution thermal cameras [ 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. Based on our targeting scenario (i.e., a subject that may wear a face mask in the stand position at the entrance gate), we consider the low-resolution thermal camera as a feasible option, as the respiratory region of interest (ROI) is significantly decreased, due to the use of a face mask.…”