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Estimation of instantaneous heart rate using video infrared thermography and ARMA models

ProQuest Dissertations and Theses, 2009
Dissertation
Author: Rodolfo Gabriel Gatto
Abstract:
Accurate and sensitive quantification of physiological systems, such as the cardiopulmonary system, historically relies on a direct contact method (i.e., placing sensors directly onto the body of the subject). However, circumstances arise when using a direct contact method of quantification is not possible or ideal. For example, individuals with tactile sensitivity, which is common in individuals with autism spectrum disorder, may not tolerate wearing sensors. Additionally, the physiology of the body may change as a result of wearing physiological sensors, thus possibly confounding the interpretation of the physiological recording. Thus, non- contact methods are needed in the evaluation of human physiology for a more objective and natural manner of extracting and examining physiological events. Video infrared thermography is a novel method that is able to record thermal activity during diverse biophysical events. We propose to use video thermography as a non-contact method of cardiovascular pulse detection. Cardiovascular activity is regulated by a delicate balance between different neuro-autonomic components. As the blood flow and perfusion of human tissues oscillates during the cardiovascular cycle, the amount of thermal energy expelled by living tissue fluctuates accordingly. Using signal processing algorithms, cardiovascular activity will be extracted from video thermography.

Table of Contents 1. SUMMARY 8 2. INTRODUCTION 9 2.1- Principles of Infrared Thermography 9 2.2- History of Infrared Thermography 12 2.3-Theory of the Infrared Thermography 17 2.4-Neuro-regulation of the Heart Beat 24 2.5- Need for a new method to asses Cardiovascular Neuroregulation 26 3. MATERIALS AND METHODS 29 3.1-Video Infrared System 29 3.2-Popul ati on of Study 30 3.3-Video Tracking System 31 3.4-Criterion Methods 32 3.5- Video infrared data processing 33 3.6-Recursive ARMA model 34 4. RESULTS 36 4.1- Results using a recursive ARMA model 36 4.2- Modeling parameters 37 4.3-Analysis of the predicted ARMA model 38 4.4- Preliminary estimation of transfer functions and forecast signal 39 2

Table of Contents (Continued) 5. DISCUSSION 41 5.1- Measuring Heart beat Neuroregulation with Video Thermography 41 5.2-Estimation of I Heart Rate Variability and ARMA models 43 5.3-Advantages and Limitations of video infrared thermography 45 6. CONCLUSIONS AND IMPLICATIONS 48 6.1-Conclusions 48 6.2-Future directions 48 7. REFERENCES 49 8. GRAPHS 61 9. TABLE 79 10. VITA 80 3

/ express my deep gratitude to my advisor Dr. Stephen Porges from the Department of Psychiatry at the University of Illinois at Chicago for providing me with interesting research opportunities while encouraging me to pursue my own path. 4

LIST OF FIGURES CHAPTER 4 Figure 1- Frequency content of pulse waves detected with photopletysmographic (Blue) and thermographic (Red) methods from the forehead. Graphs illustrate common spectral content in signals. Figure 2- Residual (yout-y) between output PPG (yout) and estimated value of thermal signal (y) using ARM A model with forgetting factors from 0.66 to 0.99. Note that residual) is minimized close to a value of 0.96. Figure 3- Distribution of Eigenvalues calculated from the ARMA model using 3500 data points. Upper figure: Distribution of cr? across subjects. Middle figure: Distribution of 02 across subjects. Lower figure: Distribution of ft across subjects. Figure 4- Interbeat intervals extracted from PPG and thermographic signals using a recursive ARMA model. Figure 5- Contrast between interbeat intervals extracted from ECG (criterion) and PPG signals in a healthy participant. Figure 6- Correlations between Interbeat intervals extracted from ECG and PPG signals in a healthy participant. Figure 7- Interbeat intervals extracted from ECG and ARMA thermal model in a healthy participant. Figure 8-Correlation between interbeat intervals extracted from ECG and ARMA thermal model in a healthy participant. Figure 9-Bland-Altman plots of interbeat intervals extracted from ECG and PPG signals in a healthy participant. Figure W-Bland-Altman plots of interbeat intervals extracted from PPG and ARMA thermal model in a healthy participant. 5

Figure 11 -Bland-Altman plots ofinterbeat intervals extracted from ECG and ARM A thermal model in a healthy participant. Figure 12- Parameters a^al), ct2=(a2) and/3 used to forecast interbeat intervals from thermographic signal. Note that most of the inter-beat interval information is contained in the beta parameter. Figure 13-S/oc/c diagram showing the steps necessary to forecast inter-beat intervals from the thermal signal. Figure 14-Upper Figure: Modeling of the (3 parameter using the autocorrelation functions with 26 data points. Lower Figure : Autocorrelation function for each participant. Figure 15-Signal forecast. Upper Figure: First 1000 data points from photoplethysmographic signal. Lower Figure: Forecasted thermography signal. Figure ^-Extraction of pulse wave from a forehead region (STA ^Superficial Temporal Artery). Note limitation of the tracking system when the head is turned 60 degrees to a lateral position (Blue arrow) and the absence of signal from the contra lateral site 6

LIST OF TABLES Table I - Average values of cti, 02 and /3 parameters based on 3500 sample points (SR=30Hz) used to fit the thermography signal. Note low dispersion of Eigen values ai and ct2 across subjects. Dispersion of /3 is larger. Table II - Correlations between forehead interbeats intervals (IBI) from PPG and thermal ARMA model (n=13). Table III - Correlations coefficients between interbeat intervals IBIs calculated from ECG, PPG and thermal (ARMA model) signals. 7

1-SUMMARY Accurate and sensitive quantification of physiological systems, such as the cardiopulmonary system, historically relies on a direct contact method (i.e., placing sensors directly onto the body of the subject). However, circumstances arise when using a direct contact method of quantification is not possible or ideal. For example, individuals with tactile sensitivity, which is common in individuals with autism spectrum disorder, may not tolerate wearing sensors. Additionally, the physiology of the body may change as a result of wearing physiological sensors, thus possibly confounding the interpretation of the physiological recording. Thus, non- contact methods are needed in the evaluation of human physiology for a more objective and natural manner of extracting and examining physiological events. Video infrared thermography is a novel method that is able to record thermal activity during diverse biophysical events. We propose to use video thermography as a non-contact method of cardiovascular pulse detection. Cardiovascular activity is regulated by a delicate balance between different neuro- autonomic components. As the blood flow and perfusion of human tissues oscillates during the cardiovascular cycle, the amount of thermal energy expelled by living tissue fluctuates accordingly. Using signal processing algorithms, cardiovascular activity will be extracted from video thermography. Superficial thermal activity will be synchronized with cardiovascular pulse activity on the forehead measured via photoplethysmography. The aim of this project is to demonstrate that video infrared thermography of the forehead can be used to measure pulse activity. Initially, we propose a methodology to quantify heart rate from the thermography data collected from the forehead. Ultimately, in future research we will evaluate the feasibility of facial thermography to monitor instantaneous beat-to-beat heart rate variability. 8

2- INTRODUCTION 2.1-Principles of Video Infrared Thermography The electromagnetic spectrum is divided arbitrarily into a number of wavelength regions called bands, which are distinguished by the methods used to generate and identify energy. At the short-wavelength end, the boundary lies at the limit of visual perception. At the long- wavelength ends it merges with the microwave radio wavelengths. Thermography makes use of the segment of light near the red color in the visible band. Specifically, it is defined as the portion of the electromagnetic spectrum in the region from 750 nm to 30,000 nm. The infrared spectrum is typically divided into three sub-bands: the reflective band (750 to 3,000 nm), thermal band (3,000 to 14,000 nm) and extreme (14,000 to 30,000 nm) band. Reflective and thermal bands are further sub divided into: near wave infrared (NWIR) 750-2,400 nm, short wave infrared (SWIR) 2,400 nm to 3,000 nm, middle wave infrared (MWIR) 3,000 to 5,000 nm and long wave infrared (LWIR) 8,000 to14,000 nm (Chekmenev et al. 2006). The actual radiation rate at a particular wavelength depends on both object temperature and emissivity. Human skin is almost a perfect black body (perfect emitter/absorber of thermal energy), having an emissivity value of 0.98 regardless of race and skin color. The wavelength power distribution radiated by a black body is given by the Planck radiation law. Considering the distribution of the power of the infrared radiation in the 8,000 to 14,000 nm band, values measured around the temperature of the human body are much higher within the LWIR band (8,000 nm to 14,000 nm) than that across the MWIR (3,000 nm to 5,000 nm) band. Our study is focused on the infrared signals in the 3,000 to 5,000 nm wavelength range. 9

Video-infrared thermography is an emergent technology in the assessment of human physiology. It has been recently employed in the scientific community as a way to monitor physiological signals in smart rooms, polygraph testing, and intent identification (Pavlidis et al. 2008). The use of video thermography in the assessment of physiological parameters represents a considerable improvement in non-contact acquisition methods for diverse biomedical applications. The passive nature of these sensors in the detection of spontaneous infrared electromagnetic energy is safe with minimal biohazard compared with other active optical and conventional methods such as spectroscopy, which imposes the irradiation of the sample with coherent light. The use of video infrared thermography as a tool in physiological research has been explored in the last decades. Previous studies have been focused in the application of this technology for physiological assessment of a diverse spectrum of signals from animals including insects (Kovac et al. 2007), reptiles, (Borrel et al. 2005) and mammals (Colak et al. 2008) in biology and veterinarian applications. Recent studies (Brandson et al 2004) have appraised the use of forehead reflectance near-infrared photo-plethysmographic (PPG) as a new tool in heartbeat monitoring. Brandson has been described this method as a more reliable assessment of heart rate than methods dependent on transmitted near-infrared photoplethysmographic signals from the finger (i.e., due to the relative inaccuracy of peripheral pulses in hypothermic or hipovolemic conditions). When both methods were compared in patients who were recovering from anesthesia, forehead photoplethysmography was faster and more sensitive to cardiovascular signals (Sugino et al. 2004). Studies have described the recovery of oscillatory thermal information from humans and the relationship of these signals to the frequency of heart beats (Branson et al. 2004). Passive thermal infrared detectors have been used to measure heart beat from the superficial blood 10

vessel network (Sun et al 2002). Based on Sun's approach, the dominant heart rate frequency was calculated by averaging the power spectra of each pixel in the preselected segment of the outer facial vessel. One of the classical limitations of this approach is the manual selection of the skin segment to be used for the heart rate measurements and the subsequent increase of the noise component due to involuntary movements of the head. An additional limitation is only the dominant heart rate frequency was reported, not the actual heart rate waveform. Currently, authors (Chekmenev et al 2006) have claimed to have obtained pulse activity and arterial pulse waveform from the arterial tree in regions of the human face using a multi-resolution wavelet- based signal analysis approach representing thermal images at different scales. In order to do so, Chekmenev selected the scale that carries the most relevant information and provides more representation to features of the region of interest (ROI) from heart signals than other scales. After several successive steps, the time varying component from each point in the ROI was removed. Using this technique, Chekmenev extracted a new representation of the heart beat by averaging and applying continuous wavelet analyses of these waveforms (Chekmenev et al 2006). 2.2- History of Infrared Thermography Less than 200 years ago the existence of the infrared portion of the electromagnetic spectrum was not suspected. The original significance of the infrared spectrum, or simply 'the infrared' as it is often called, as a form of heat radiation is perhaps less obvious today than it was at the time of its discovery by Herschel in 1800. 11

The discovery was made accidentally during the search for a new optical material. Sir William Herschel was searching for an optical filter material to reduce the brightness of the sun's image in telescopes during solar observations. While testing different samples of colored glass which gave similar reduction in brightness he was intrigued to find that some of the samples passed very little of the sun's heat, while others passed so much heat that he risked eye damage after only a few seconds' observation. Herschel was soon convinced of the necessity of setting up a systematic experiment, with the objective of finding a single material that would give the desired reduction in brightness as well as the maximum reduction in heat. He began the experiment by actually repeating Newton's prism experiment, but looking for the heating effect rather than the visual distribution of intensity in the spectrum. He first blackened the bulb of a sensitive mercury-in-glass thermometer with ink, and with this as his radiation detector he proceeded to test the heating effect of the various colors of the spectrum formed on the top of a table by passing sunlight through a glass prism. Other thermometers, placed outside the sun's rays, served as controls. As the blackened thermometer was moved slowly along the colors of the spectrum, The temperature readings showed a steady increase from the violet end to the red end. This was not entirely unexpected, since the Italian researcher, Landriani, in a similar experiment in 1777, observed the same effect. It was Herschel however, who was the first to identify the point at which the heating effect reaches a maximum, and that measurements confined to the visible portion of the spectrum failed to locate this point. Moving the thermometer into the dark region beyond the red end of the spectrum, 12

Herschel established that the heating continued to increase. The maximum point, when he found it, lay well beyond the red end—in what is known today as the 'infrared wavelengths.' When Herschel revealed his finding, he referred to this new portion of the electromagnetic spectrum as the 'thermometrical spectrum". The radiation itself he sometimes referred to as 'dark heat,' or simply 'the invisible rays' ironically, and contrary to popular opinion, it wasn't Herschel who originated the term 'infrared.' The word only began to appear in print around 75 years later, and it is still unclear who should receive credit as the originator. Herschel's use of glass in the prism of his original experiment led to some early controversies with his contemporaries about the actual existence of the infrared wavelengths. Different investigators, in attempting to confirm his work, used various types of glass indiscriminately, having different transparencies in the infrared. Through his later experiments, Herschel was aware of the limited transparency of glass to the newly-discovered thermal radiation, and he was forced to conclude that optics for the infrared would probably be doomed to the use of reflective elements exclusively (i.e. plane and curved mirrors). Fortunately, this proved to be true only until 1830, when the Italian investigator, Melloni, made his great discovery that naturally occurring rock salt (NaCI)—which was available in large enough natural crystals to be made into lenses and prisms—is remarkably transparent to the infrared. The result was that rock salt became the principal infrared optical material, and remained so for the next hundred years, until the art of synthetic crystal growing was mastered in the 1930's. Thermometers, as radiation detectors, remained unchallenged until 1829, the year Nobili invented the thermocouple. (Herschel's own thermometer could be read to 0.2°C (0.036°F), and later models were able to be read to 0.05°C (0.09°F). Then a breakthrough 13

occurred, when Melloni connected a number of thermocouples in series to form the first thermopile. The new device was at least 40 times as sensitive as the best thermometer of the day for detecting heat radiation—capable of detecting the heat from a person standing 3 meters away (10 ft.). The first so-called 'heat-picture' became possible in 1840, the result of work by Sir John Herschel, son of the discoverer of infrared and a famous astronomer in his own right. Based upon the differential evaporation of a thin film of oil when exposed to a heat pattern focused upon it, the thermal image could be seen by reflected light where the interfering effects of the oil film made the image visible to the eye. Sir John also managed to obtain a primitive record of the thermal image on paper, which he called a 'thermograph.' The development of infrared-detector sensitivity progressed slowly. Another major breakthrough, made by Langley in 1880, was the invention of the bolometer. This consisted of a thin blackened strip of platinum connected in one arm of a Wheatstone bridge circuit upon which the infrared radiation was focused and to which a sensitive galvanometer responded. This instrument is said to have been able to detect the heat from a cow at a distance of 400 meters An English scientist, Sir James Dewar, first introduced the use of liquefied gases as cooling agents (such as liquid nitrogen with a temperature of -196°C (-320.8°F)) in low temperature research. In 1892 he invented an exclusive vacuum insulating container in which it is feasible to store liquefied gases for entire days. The common 'thermos bottle', used for storing hot and cold drinks, is based upon his invention. Between the years 1900 and 1920, inventors 'discovered' infrared and several patents were issued for devices to detect personnel, artillery, 14

aircraft, ships—and even icebergs. The first operating systems, in the modern sense, began to be developed during World War I, when both sides had research programs devoted to the military exploitation of infrared. These programs included experimental systems for enemy intrusion/detection, remote temperature sensing, secure communications, and 'flying torpedo' guidance. An infrared search system tested during this period was able to detect an approaching airplane at a distance of 1.5 km (0.94 miles), or a person more than 300 meters (984 ft.) away. The most sensitive systems up to this time were all based upon variations of the bolometer idea, but the period between the two world wars saw the development of two revolutionary new infrared detectors: the image converter and the photon detector. At first, the image converter received the greatest attention by the military, because it enabled an observer for the first time in history to literally 'see in the dark.' However, the sensitivity of the image converter was limited to the near infrared wavelengths, and the most interesting military targets (i.e. enemy soldiers) had to be illuminated by infrared search beams. Since this involved the risk of giving away the observer's position to a similarly-equipped enemy observer, it is understandable that military interest in the image converter eventually faded. The tactical military disadvantages of so-called 'active' (i.e. search beam-equipped) thermal imaging systems provided impetus following World War II for extensive secret military infrared- research programs into the possibilities of developing 'passive' (no search beam) systems around the extremely sensitive photon detector. 15

During this period, military secrecy regulations completely prohibited disclosure of the status of infrared- Imaging technology. This secrecy only began to be lifted in the middle of the 1950's, and from that time adequate thermal-imaging devices finally began to be available to civilian science and industry (Flir infrared manual 2006). 2.3- Theory of the Infrared Thermography 2.3.1 - Blackbody radiation A blackbody is defined as an object, which absorbs all radiation that impinges on it at any wavelength. The apparent misnomer black, relates to an object emitting radiation is explained by Kirchhoff s Law (after Gustav Robert Kirchhoff, 1824-1887). The law states that a body capable of absorbing all radiation at any wavelength is equally capable in the emission of radiation. The assembly of a blackbody source is, in principle, very simple. The radiation characteristics of an aperture in an isotherm cavity made of an opaque absorbing material represents almost exactly the properties of a blackbody. A practical application of the principle to the building of a perfect absorber of radiation consists of a box that is light tight except for an aperture in one of the sides. Any radiation, which then enters the hole, is scattered and absorbed by frequent reflections so only an infinitesimal fraction can possibly escape. The blackness, which is obtained at the aperture, is nearly equal to a blackbody and almost ideal for all wavelengths. By providing such an isothermal cavity with a suitable heater it becomes what is termed a 16

cavity radiator. An isothermal cavity heated to a uniform temperature generates blackbody radiation, the characteristics of which are determined solely by the temperature of the cavity. Such cavity radiators are commonly used as sources of radiation in temperature reference standards in the laboratory for calibrating thermographic instruments, such as a FLIR Systems camera for example. If the temperature of blackbody radiation increases to more than 525 °C (977 °F), the source begins to be visible so that it appears to the eye no longer black. This is the incipient red heat temperature of the radiator, which then becomes orange or yellow as the temperature increases further. In fact, the definition of the so-called color temperature of an object is the temperature to which a blackbody would have to be heated to have the same appearance. Now consider three expressions that describe the radiation emitted from a blackbody. 2.3.2 Planck's law Max Planck (1858-1947) was able to describe the spectral distribution of the radiation from a blackbody by means of the following formula: 97rhr2 W» = -TTWTT V X W~'\Watt I m\ fitn] X e - II x ' Equation 1 Where: WAb =Blackbody spectral radiant emittance at wavelength A. c =Velocity of light = 3 * 108 m/s 17

h =Planck's constant = 6.6 * 10-34 Joule sec. k =Boltzmann's constant = 1.4 * 10-23 Joule/K. T =Absolute temperature (K) of a blackbody. A =Wavelength (urn). Planck's formula, when plotted graphically for various temperatures, produces a family of curves. Following any particular Planck curve, the spectral emittance is zero at A = 0, then increases rapidly to a maximum at a wavelength Amax and after passing it approaches zero again at very long wavelengths. The higher the temperature, the shorter the wavelength at which maximum occurs. 2.3.3 Wien's displacement law By differentiating Planck's formula with respect to A, and finding the maximum, we have: 2898, Equation 2 This is Wien's formula (after Wilhelm Wien, 1864-1928), which expresses mathematically the common observation that colors vary from red to orange or yellow as the temperature of a thermal radiator increases. The wavelength of the color is the same as the wavelength calculated for Amax. A good approximation of the value of Amax for a given blackbody temperature is obtained by applying the rule-of-thumb 3,000/T urn. Consequently, a very hot star such as Sirius (11,000 K°), emitting bluish-white light, radiates with the peak of spectral radiant emittance occurring within the invisible ultraviolet spectrum, at wavelength 0.27 urn. The sun (approx. 6,000 K°) emits yellow light, peaking at about 0.5 urn in the middle of the 18

visible light spectrum. At room temperature (300 K°) the peak of radiant emittance lies at 9.7 urn, in the far infrared, while at the temperature of liquid nitrogen (77 K°) the maximum of the almost insignificant amount of radiant emittance occurs at 38 urn, in the extreme infrared wavelengths. 2.3.4 Stefan-Boltzmann's law By integrating Planck's formula from A = 0 to A = °°, we obtain the total radiant emittance (Wb) of a blackbody: Wb = aT4 [Watt/m; Equation 3 This is the Stefan-Boltzmann formula (after Josef Stefan, 1835-1893, and Ludwig Boltzmann, 1844-1906), which states that the total emissive power of a blackbody is proportional to the fourth power of its absolute temperature. Graphically, Wb represents the area below the Planck curve for a particular temperature. It can be shown that the radiant emittance in the interval A = 0 to Amax is only 25 % of the total, which represents about the amount of the sun's radiation which lies inside the visible light spectrum. Using the Stefan-Boltzmann formula to calculate the power radiated by the human body, at a temperature of 300 K° and an external surface area of approx. 2 m2, we obtain 1 kW. This power loss could not be sustained, if it were not for the compensating absorption of radiation from surrounding surfaces. At room temperatures the absorption of radiation does not vary 19

drastically from the temperature of the body. 2.3.6 Non-blackbody emitters So far, only blackbody radiators and blackbody radiation have been discussed. Nevertheless, real objects almost never comply with these laws over an extended wavelength region, although they may approach the blackbody behavior in certain spectral intervals. For example, a certain type of white paint may appear perfectly white in the visible light spectrum, but becomes distinctly gray at about 2 um, and beyond 3 urn it is almost black. There are three processes, which can occur that prevent a real object from acting like a blackbody: 1) a fraction of the incident radiation a may be absorbed, 2) a fraction p may be reflected, and 3) a fraction T may be transmitted. Since all of these factors are more or less wavelength dependent, the subscript A is used to imply the spectral dependence of their definitions. 2.3.7 - Infrared semi-transparent materials Let us consider a non-metallic, semi-transparent body in the form of a thick flat plate of plastic material. When the plate is heated, radiation generated within its volume must work its way toward the surfaces through the material in which it is partially absorbed. Moreover, when it arrives at the surface, some of it is reflected back into the interior. The back-reflected radiation is again partially absorbed. Some of the radiation arrives at the other surface, through which most of it escapes and part is reflected back again. Although the progressive reflections become weaker and weaker they must all be summed to quantify the total emittance of the plate. When the resulting geometrical series is summed, the effective emissivity of a semitransparent plate is 20

obtained. When the plate becomes opaque this formula is reduced to the single formula. This last relation is a particularly convenient one, because it is often easier to measure reflectance than to measure emissivity directly. Thermoregulation is manifested in the pattern of skin temperature in selected areas of the body (Van den Heulven et al. 2003). Undisturbed central and peripheral regulation mechanisms maintain a relatively steady core body temperature and are independent of the environmental temperature. The hypothalamus, a brain structure involved in thermoregulation, processes information from external and internal thermoreceptors leading in the adjustment target temperatures. In addition to this feedback loop, phasic adjustments of the target temperature occur and are observed as circadian rhythms. The temperature profile is subject to various influences, including arteriosclerosis, sympathetic tone, heat and water metabolisms, thickness and pigmentation of the skin, and periodical fluctuations in hormone levels,. Recent work also demonstrates asymmetry in facial temperature (Rustemeyer et al. 2007)and circadian rhythms in surface temperature of the face. A circadian rhythm with a temperature minimum at between 2 AM and 6 AM and a maximum at between 6 PM and 10 PM has been observed in the face. In addition, comparison of symmetrically paired values from the two sides of the face reveal a distinct asymmetry indicating that temperature on the left side of the face is 0.1 °C lower (Rustemeyer et al. 2007). Recent interest in the forehead area has used facial thermography to study sustained stress and deception. In the forehead, heat is convected from the flow of 'hot' arterial blood in superficial facial vasculature radiating through the skin. Therefore, one can record and analyze these heat signals through a thermal imaging sensor of sufficient sensitivity. When a subject 21

experiences stress during the interrogation, more blood flows through the supraorbital vasculature and increases the cutaneous forehead temperature. In order to monitor the thermal signature of forehead tissue, a segmentation method based on active contours has been developed creating a virtual forehead probe that can monitor stress levels by measuring thermal radiation over the supraorbital vessels (Zhu et al 2009). As the facial physiology changes locally with the onset of stress, scientists have demonstrated that the onset of autonomic stress is associated with instantaneous changes in the blood flow supply of the obicularis oculi muscle. Researcher have proposed a segmentation method that localizes the thermal footprint of the facial and ophthalmic arterial-venous complexes in the periorbital area. The periorbital tissue is irrigated by the facial and ophthalmic arterial-venous complexes, supplying with blood the orbicularis oculi muscle (Shastri et al 2008). Other current field of work in facial thermography is investigating novel methods for face recognition. These studies intend to take advantage of the permanency of innate individuality that muscles and bone structure has under the skin. The specific methodology to capture bioheat information contained in thermal imagery includes algorithms that delineate the human face from the background using the Bayesian framework and localizes the superficial blood vessel network using image morphology. The extracted vascular network produces contour shapes that are characteristic to each individual. The branching points of the skeletonized vascular network are collected for each subject and stored in five different pose images matching the local thermal points to structures extracted from the test image with those of the corresponding pose images in the database (Buddharaju et al. 2007). 22

2.4 - Neuro-regulation of the heart The vagus is the 10th cranial nerve and contains pathways that contribute to the regulation of the internal viscera, including the heart. Vagal efferent fibers do not originate in a common brainstem structure. The Polyvagal Theory is introduced to explain the different functions of the two primary medullary source nuclei of the vague: the nucleus ambiguus (NA) and the dorsal motor nucleus of the vagus (DMNX). Vagal pathways from both nuclei terminate on the heart's pacemaker (i.e., sinoatrial node). Fibers originating in NA are uniquely responsible for respiratory sinus arrhythmia (RSA), a naturally occurring rhythm in heart rate (see below). Divergent shifts in RSA and heart rate are explained by independent actions of DMNX and NA. The theory emphasizes a phylogenetic perspective and speculates that mammalian, but not reptilian, brainstem organization, which is characterized by a ventral vagal complex (including NA), is related to processes associated with attention, motion, emotion, and communication. Various clinical disorders, such as sudden infant death syndrome and asthma, may be related to the competition between DMNX and NA (Porges 1994). There is an increase in heart rate frequency or cardio-acceleration during inspiration and a decrease in heart rate or cardio-deceleration during expiration. Since this periodic phenomenon is related to the respiratory cycle, it is known as respiratory sinus arrhythmia (RSA). Heart beats fluctuates with the phases of respiration, the interbeat intervals increase and decrease with exhilation and inhalation. RSA is predominantly mediated by a central nervous system circuit located in the medulla, which stimulates through a complex neuronal network the respiratory gating of parasympathetic efferent activity to the heart (Riniolo et al. 1997, Porges et al. 1982, Jennings et al. 1981). The reduction of RSA has been used as an indicator of reduced 23

Full document contains 85 pages
Abstract: Accurate and sensitive quantification of physiological systems, such as the cardiopulmonary system, historically relies on a direct contact method (i.e., placing sensors directly onto the body of the subject). However, circumstances arise when using a direct contact method of quantification is not possible or ideal. For example, individuals with tactile sensitivity, which is common in individuals with autism spectrum disorder, may not tolerate wearing sensors. Additionally, the physiology of the body may change as a result of wearing physiological sensors, thus possibly confounding the interpretation of the physiological recording. Thus, non- contact methods are needed in the evaluation of human physiology for a more objective and natural manner of extracting and examining physiological events. Video infrared thermography is a novel method that is able to record thermal activity during diverse biophysical events. We propose to use video thermography as a non-contact method of cardiovascular pulse detection. Cardiovascular activity is regulated by a delicate balance between different neuro-autonomic components. As the blood flow and perfusion of human tissues oscillates during the cardiovascular cycle, the amount of thermal energy expelled by living tissue fluctuates accordingly. Using signal processing algorithms, cardiovascular activity will be extracted from video thermography.