Estimating Carotid Pulse and Breathing Rate from Near-infrared Video of the Neck

Objective: Non-contact physiological measurement is a growing research area that allows capturing vital signs such as heart rate (HR) and breathing rate (BR) comfortably and unobtrusively with remote devices. However, most of the approaches work only in bright environments in which subtle photoplethysmographic and ballistocardiographic signals can be easily analyzed and/or require expensive and custom hardware to perform the measurements. Approach: This work introduces a low-cost method to measure subtle motions associated with the carotid pulse and breathing movement from the neck using near-infrared (NIR) video imaging. A skin reflection model of the neck was established to provide a theoretical foundation for the method. In particular, the method relies on template matching for neck detection, Principal Component Analysis for feature extraction, and Hidden Markov Models for data smoothing. Main Results: We compared the estimated HR and BR measures with ones provided by an FDA-cleared device in a 12-participant laboratory study: the estimates achieved a mean absolute error of 0.36 beats per minute and 0.24 breaths per minute under both bright and dark lighting. Significance: This work advances the possibilities of non-contact physiological measurement in real-life conditions in which environmental illumination is limited and in which the face of the person is not readily available or needs to be protected. Due to the increasing availability of NIR imaging devices, the described methods are readily scalable.Comment: 21 pages, 15 figure

Twente Optical Perfusion Camera: system overview and performance for video rate laser Doppler perfusion imaging

We present the Twente Optical Perfusion Camera (TOPCam), a novel laser Doppler Perfusion Imager based on CMOS technology. The tissue under investigation is illuminated and the resulting dynamic speckle pattern is recorded with a high speed CMOS camera. Based on an overall analysis of the signal-to-noise ratio of CMOS cameras, we have selected the camera which best fits our requirements. We applied a pixel-by-pixel noise correction to minimize the influence of noise in the perfusion images. We can achieve a frame rate of 0.2 fps for a perfusion image of 128×128 pixels (imaged tissue area of 7×7 cm2) if the data is analyzed online. If the analysis of the data is performed offline, we can achieve a frame rate of 26 fps for a duration of 3.9 seconds. By reducing the imaging size to 128×16 pixels, this frame rate can be achieved for up to half a minute. We show the fast imaging capabilities of the system in order of increasing perfusion frame rate. First the increase of skin perfusion after application of capsicum cream, and the perfusion during an occlusion-reperfusion procedure at the fastest frame rate allowed with online analysis is shown. With the highest frame rate allowed with offline analysis, the skin perfusion revealing the heart beat and the perfusion during an occlusion-reperfusion procedure is presented. Hence we have achieved video rate laser Doppler perfusion imaging

Universal Arduino-based experimenting system to support teaching of natural sciences

The rapid evolution of intelligent electronic devices makes information technology, computer science and electronics strongly related to the teaching of natural sciences. Today almost everybody has a smart phone that can convert light, temperature, movement, sound to numbers, therefore all these can be processed, analysed, displayed, stored, shared by software applications. The fundamental question is how education can follow this knowledge and how can education take its advantages. Components and methods of modern technology are available for education also, teachers and students can play with parts and tools which were previously used only by engineers. A good example is the very popular Arduino board which is practically an industrial microcontroller whose pins are wired to easy-to-use connectors on a printed circuit board. In this paper we show a universal system which we have developed for the Arduino platform to support experimenting and understanding of the most fundamental principles of the operation of modern devices. We show our related educational concept and discuss the most important features of the system. Open source hardware and software are available and we provide a number of video tutorials as well

Video Compressive Sensing for Dynamic MRI

We present a video compressive sensing framework, termed kt-CSLDS, to accelerate the image acquisition process of dynamic magnetic resonance imaging (MRI). We are inspired by a state-of-the-art model for video compressive sensing that utilizes a linear dynamical system (LDS) to model the motion manifold. Given compressive measurements, the state sequence of an LDS can be first estimated using system identification techniques. We then reconstruct the observation matrix using a joint structured sparsity assumption. In particular, we minimize an objective function with a mixture of wavelet sparsity and joint sparsity within the observation matrix. We derive an efficient convex optimization algorithm through alternating direction method of multipliers (ADMM), and provide a theoretical guarantee for global convergence. We demonstrate the performance of our approach for video compressive sensing, in terms of reconstruction accuracy. We also investigate the impact of various sampling strategies. We apply this framework to accelerate the acquisition process of dynamic MRI and show it achieves the best reconstruction accuracy with the least computational time compared with existing algorithms in the literature.Comment: 30 pages, 9 figure

A perpetual switching system in pulmonary capillaries

Of the 300 billion capillaries in the human lung, a small fraction meet normal oxygen requirements at rest, with the remainder forming a large reserve. The maximum oxygen demands of the acute stress response require that the reserve capillaries are rapidly recruited. To remain primed for emergencies, the normal cardiac output must be parceled throughout the capillary bed to maintain low opening pressures. The flow-distributing system requires complex switching. Because the pulmonary microcirculation contains contractile machinery, one hypothesis posits an active switching system. The opposing hypothesis is based on passive switching that requires no regulation. Both hypotheses were tested ex vivo in canine lung lobes. The lobes were perfused first with autologous blood, and capillary switching patterns were recorded by videomicroscopy. Next, the vasculature of the lobes was saline flushed, fixed by glutaraldehyde perfusion, flushed again, and then reperfused with the original, unfixed blood. Flow patterns through the same capillaries were recorded again. The 16-min-long videos were divided into 4-s increments. Each capillary segment was recorded as being perfused if at least one red blood cell crossed the entire segment. Otherwise it was recorded as unperfused. These binary measurements were made manually for each segment during every 4 s throughout the 16-min recordings of the fresh and fixed capillaries (>60,000 measurements). Unexpectedly, the switching patterns did not change after fixation. We conclude that the pulmonary capillaries can remain primed for emergencies without requiring regulation: no detectors, no feedback loops, and no effectors-a rare system in biology. NEW & NOTEWORTHY The fluctuating flow patterns of red blood cells within the pulmonary capillary networks have been assumed to be actively controlled within the pulmonary microcirculation. Here we show that the capillary flow switching patterns in the same network are the same whether the lungs are fresh or fixed. This unexpected observation can be successfully explained by a new model of pulmonary capillary flow based on chaos theory and fractal mathematics

Expert-Augmented Machine Learning

Machine Learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption by the level of trust that models afford users. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of man and machine. Here we present Expert-Augmented Machine Learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We use a large dataset of intensive care patient data to predict mortality and show that we can extract expert knowledge using an online platform, help reveal hidden confounders, improve generalizability on a different population and learn using less data. EAML presents a novel framework for high performance and dependable machine learning in critical applications