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.

Adaptive wake and sleep detection for wearable systems

Walter Karlen

Sleep problems and disorders have a serious impact on human health and wellbeing. The rising costs for treating sleep-related chronic diseases in industrialized countries demands efficient prevention. Low-cost, wearable sleep / wake detection systems which give feedback on the wearer's "sleep performance" are a promising approach to reduce the risk of developing serious sleep disorders and fatigue. Not all bio-medical signals that are useful for sleep / wake discrimination can be easily recorded with wearable systems. Sensors often need to be placed in an obtrusive location on the body or cannot be efficiently embedded into a wearable frame. Furthermore, wearable systems have limited computational and energetic resources, which restrict the choice of sensors and algorithms for online processing and classification. Since wearable systems are used outside the laboratory, the recorded signals tend to be corrupted with additional noise that influences the precision of classification algorithms. In this thesis we present the research on a wearable sleep / wake classifier system that relies on cardiorespiratory (ECG and respiratory effort) and activity recordings and that works autonomously with minimal user interaction. This research included the selection of optimal signals and sensors, the development of a custom-tailored hardware demonstrator with embedded classification algorithms, and the realization of experiments in real-world environments for the customization and validation of the system. The processing and classification of the signals were based on Fourier transformations and artificial neural networks that are efficiently implementable into digital signal controllers. Literature analysis and empiric measurements revealed that cardiorespiratory signals are more promising for a wearable sleep / wake classification than clinically used signals such as brain potentials. The experiments conducted during this thesis showed that inter-subject differences within the recorded physiological signals make it difficult to design a sleep / wake classification model that can generalize to a group of subjects. This problem was addressed in two ways: First by adding features from another signal to the classifier, that is, measuring the behavioral quiescence during sleep using accelerometers. Conducted research on different feature extraction methods from accelerometer data showed that this data generalizes well for distinct subjects in the study group. In addition, research on user-adaptation methods was conducted. Behavioral sleep and wake measures, notably the measurement of reactivity and activity, were developed to build up a priori knowledge that was used to adapt the classification algorithm automatically to new situations. This thesis demonstrates the design and development of a low-cost, wearable hardware and embedded software for on-line sleep / wake discrimination. The proposed automatic user-adaptive classifier is advantageous compared to previously suggested classification methods that generalize over multiple subjects, because it can take changes in the wearer's physiology and sleep / wake behavior into account without adjustment from a human expert. The results of this thesis contribute to the development of smart, wearable, bio-physiological monitoring systems which require a high degree of autonomy and have only low computational resources available. We believe that the proposed sleep / wake classification system is a first promising step toward a context-aware system for sleep management, sleep disorder prevention, and reduction of fatigue.

EPFL2009

Robust, High-Speed Interest Point Matching for Real-Time Applications

Michael Calonder

A typical Computer Vision system needs to process vast amounts of data as captured by one or more cameras, constantly testing the capabilities of today's hardware. Yet such systems face an ever-growing computational load caused by the more and more demanding applications which is further amplified by the trend to run the software on computationally weaker devices such as mobile phones, for example. Moreover, it is imperative for most real-world systems, be it for object detection or mobile robotics, to run in real-time so as to serve the purpose. Although applications of such systems can vary greatly, most systems share the same outset in the processing pipeline: They establish sparse point matches across multiple views of rigid scenes, which is known as the interest point matching problem. Being a very central problem, a number of algorithms offer fairly accurate solutions. However, they force their superordinate application to spend most of the available CPU time on matching, leaving only little room for subsequent tasks in the pipeline. In this thesis we propose two families of algorithms, Signatures and BRIEF, that address these difficulties. Both of them exhibit comparable accuracy to existing methods and improve up to two orders of magnitude on overall speed and one order of magnitude on memory consumption. Signatures and especially BRIEF are conceptually of downright simplicity, offering a competitive and easy-to-implement option for building a solid base for real-time applications with a stringent time budget. We demonstrate the effectiveness by implementing Signatures and BRIEF in four real-time systems. Technically, Signatures employ Randomized Trees to quantify the similarity of two image patches around arbitrary interest points, resulting in a robust description vector. Thanks to the binary tests that the Trees use for indexing image patches into the leaves, they are very efficient to evaluate which directly carries over to Signatures. BRIEF is the ultimate reduction of the binary test idea and uses such tests directly on a smoothed version of the patch to describe it. Doing so can be seen as computing thresholded random gradients resulting in binary description vectors which require less memory than their state-of-the-art counterparts and are particularly efficient to match —especially on recent hardware.

EPFL2010

The PANOPTIC Camera: A Plenoptic Sensor with Real-Time Omnidirectional Capability

Hossein Afshari, Luigi Bagnato, Laurent Jacques, Yusuf Leblebici, Alexandre Schmid, Pierre Vandergheynst

A new biologically-inspired vision sensor made of one hundred "eyes" is presented, which is suitable for real-time acquisition and processing of 3-D image sequences. This device, named the Panoptic camera, consists of a layered arrangement of approximately 100 classical CMOS imagers, distributed over a hemisphere of 13cm in diameter. The Panoptic camera is a polydioptric system where all imagers have their own vision of the world, each with a distinct focal point, which is a specific feature of the Panoptic system. This enables 3-D information recording such as omnidirectional stereoscopy or depth estimation, applying specific signal processing. The algorithms dictating the image reconstruction of an omnidirectional observer located at any point inside the hemisphere are presented. A hardware architecture which has the capability of handling these algorithms, and the flexibility to support additional image processing in real time, has been developed as a two-layer system based on FPGAs. The detail of the hardware architecture, its internal blocks, the mapping of the algorithms onto the latter elements, and the device calibration procedure are presented, along with imaging results.