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

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

Signal Processing Approaches for Cardio-Respiratory Biosignals with an Emphasis on Mobile Health Applications

We humans are constantly preoccupied with our health and physiological status. From precise measurements such as the 12-lead electrocardiograms recorded in hospitals, we have moved on to mobile acquisition devices, now as versatile as smart-watches and smart-phones. Established signal processing techniques do not cater to the particularities of mobile biomedical health monitoring applications. Moreover, although our capabilities to acquire data are growing, many underlying physiological phenomena remain poorly understood. This thesis focuses on two aspects of biomedical signal processing. First, we investigate the physiological basis of the relationship between cardiac and breathing biosignals. Second, we propose a methodology to understand and use this relationship in health monitoring applications. Part I of this dissertation examines the physiological background of the cardio-respiratory relationship and indexes based on this relationship. We propose a methodology to extract the respiratory sinus arrhythmia (RSA), which is an important aspect of this relationship. Furthermore, we propose novel indexes incorporating dynamics of the cardio-respiratory relationship, using the RSA and the phase lag between RSA and breathing. We then evaluate, systematically, existing and novel indexes under known autonomic stimuli. We demonstrate our indexes to be viable additions to the existing ones, thanks to their performance and physiological merits. Part II focuses on real-time and instantaneous methods for the estimation of the breathing parameters from cardiac activity, which is an important application of the cardio-respiratory relationship. The breathing rate is estimated from electrocardiogram and imaging photoplethysmogram recordings, using two dedicated filtering schemes, one of which is novel. Our algorithm measures this important vital rhythm in a truly real-time manner, with significantly shorter delays than existing methods. Furthermore, we identify situations, in which an important assumption regarding the estimation of breathing parameters from cardiac activity does not hold, and draw a road-map to overcome this problem. In Part III, we use indexes and methodology developed in Parts I and II in two applications for mobile health monitoring, namely, emotion recognition and sleep apnea detection from cardiac and breathing biosignals. Results on challenging datasets show that the cardio-respiratory indexes introduced in the present thesis, especially those related to the phase lag between RSA and breathing, are successful for emotion recognition and sleep apnea detection. The novel indexes reveal to be complementary to previous ones, and bring additional insight into the physiological basis of emotions and apnea episodes. To summarize, the techniques proposed in this thesis help to bypass shortcomings of previous approaches in the understanding and the estimation of cardio-respiratory coupling in real-life mobile health monitoring