Breathing Pattern Monitoring by Using Remote Sensors.

The ability to continuously and unobtrusively monitor and classify breathing patterns can be very valuable for automated health assessments because respiration is tightly coupled to many physiological processes. Pathophysiological changes in these processes often manifest in altered breathing patterns and can thus be immediately detected. In order to develop a breathing pattern monitoring system, a study was conducted in which volunteer subjects were asked to breathe according to a predefined breathing protocol containing multiple breathing patterns while being recorded with color and thermal cameras. The recordings were used to develop and compare several respiratory signal extraction algorithms. An algorithm for the robust extraction of multiple respiratory features was developed and evaluated, capable of differentiating a wide range of respiratory patterns. These features were used to train a one vs. one multiclass support vector machine, which can distinguish between breathing patterns with an accuracy of 95.79 %. The recorded dataset was published to enable further improvement of contactless breathing pattern classification, especially for complex breathing patterns.

Consciousness Detection on Injured Simulated Patients Using Manual and Automatic Classification via Visible and Infrared Imaging.

In a disaster scene, triage is a key principle for effectively rescuing injured people according to severity level. One main parameter of the used triage algorithm is the patient's consciousness. Unmanned aerial vehicles (UAV) have been investigated toward (semi-)automatic triage. In addition to vital parameters, such as heart and respiratory rate, UAVs should detect victims' mobility and consciousness from the video data. This paper presents an algorithm combining deep learning with image processing techniques to detect human bodies for further (un)consciousness classification. The algorithm was tested in a 20-subject group in an outside environment with static (RGB and thermal) cameras where participants performed different limb movements in different body positions and angles between the cameras and the bodies' longitudinal axis. The results verified that the algorithm performed better in RGB. For the most probable case of 0 degrees, RGB data obtained the following results: Mathews correlation coefficient (MMC) of 0.943, F1-score of 0.951, and precision-recall area under curve AUC (PRC) score of 0.968. For the thermal data, the MMC was 0.913, F1-score averaged 0.923, and AUC (PRC) was 0.960. Overall, the algorithm may be promising along with others for a complete contactless triage assessment in disaster events during day and night.

An Anatomical Thermal 3D Model in Preclinical Research: Combining CT and Thermal Images.

Even though animal trials are a controversial topic, they provide knowledge about diseases and the course of infections in a medical context. To refine the detection of abnormalities that can cause pain and stress to the animal as early as possible, new processes must be developed. Due to its noninvasive nature, thermal imaging is increasingly used for severity assessment in animal-based research. Within a multimodal approach, thermal images combined with anatomical information could be used to simulate the inner temperature profile, thereby allowing the detection of deep-seated infections. This paper presents the generation of anatomical thermal 3D models, forming the underlying multimodal model in this simulation. These models combine anatomical 3D information based on computed tomography (CT) data with a registered thermal shell measured with infrared thermography. The process of generating these models consists of data acquisition (both thermal images and CT), camera calibration, image processing methods, and structure from motion (SfM), among others. Anatomical thermal 3D models were successfully generated using three anesthetized mice. Due to the image processing improvement, the process was also realized for areas with few features, which increases the transferability of the process. The result of this multimodal registration in 3D space can be viewed and analyzed within a visualization tool. Individual CT slices can be analyzed axially, sagittally, and coronally with the corresponding superficial skin temperature distribution. This is an important and successfully implemented milestone on the way to simulating the internal temperature profile. Using this temperature profile, deep-seated infections and inflammation can be detected in order to reduce animal suffering.

Monitoring of Cardiorespiratory Signals Using Thermal Imaging: A Pilot Study on Healthy Human Subjects.

Heart rate (HR) and respiratory rate (RR) are important parameters for patient assessment. However, current measurement techniques require attachment of sensors to the patient’s body, often leading to discomfort, stress and even pain. A new algorithm is presented for monitoring both HR and RR using thermal imaging. The cyclical ejection of blood flow from the heart to the head (through carotid arteries and thoracic aorta) leads to periodic movements of the head; these vertical movements are used to assess HR. Respiratory rate is estimated by using temperature fluctuations under the nose during the respiratory cycle. To test the viability and feasibility of this approach, a pilot study was conducted with 20 healthy subjects (aged 18⁻36 and 1 aged 50 years). The study consisted of two phases: phase A (frontal view acquisitions) and phase B (side view acquisitions). To validate the results, photoplethysmography and thoracic effort (piezoplethysmography) were simultaneously recorded. High agreement between infrared thermography and ground truth/gold standard was achieved. For HR, the root-mean-square errors (RMSE) for phases A and B were 3.53 ± 1.53 and 3.43 ± 1.61 beats per minute, respectively. For RR, the RMSE between thermal imaging and piezoplethysmography stayed around 0.71 ± 0.30 breaths per minute (phase A). This study demonstrates that infrared thermography may be a promising, clinically relevant alternative for the assessment of HR and RR.

Estimating Respiratory Rate in Post-Anesthesia Care Unit Patients Using Infrared Thermography: An Observational Study.

The post-anesthesia care unit (PACU) is the central hub for recovery after surgery, especially when the surgery is performed under general anesthesia. Aside from clinical aspects, respiratory impairment is one of the major causes of morbidity and affected recovery in the PACU and should therefore be monitored. In previous studies, infrared thermography was applied to assess the breathing rate (BR) of healthy volunteers. Here, the transferability of published methods for postoperative patients in the PACU was examined. Video recordings of 28 patients were acquired using a long-wave infrared camera, and analyzed offline. For validation purposes, BRs derived from body surface electrocardiography were measured simultaneously. In general, a close agreement between the two techniques (r = 0.607, p = 0.002 upon arrival, and r = 0.849, p < 0.001 upon discharge from the PACU) was obtained. In conclusion, the algorithm was demonstrated to be feasible and reliable under these challenging conditions.

Estimation of breathing rate in thermal imaging videos: a pilot study on healthy human subjects.

Diverse studies have demonstrated the importance of monitoring breathing rate (BR). Commonly, changes in BR are one of the earliest and major markers of serious complications/illness. However, it is frequently neglected due to limitations of clinically established measurement techniques, which require attachment of sensors. The employment of adhesive pads or thoracic belts in preterm infants as well as in traumatized or burned patients is an additional paramount issue. The present paper proposes a new robust approach, based on data fusion, to remotely monitor BR using infrared thermography (IRT). The algorithm considers not only temperature modulation around mouth and nostrils but also the movements of both shoulders. The data of these four sensors/regions of interest need to be further fused to reach improved accuracy. To investigate the performance of our approach, two different experiments (phase A: normal breathing, phase B: simulation of breathing disorders) on twelve healthy volunteers were performed. Thoracic effort (piezoplethysmography) was simultaneously acquired to validate our results. Excellent agreements between BR estimated with IRT and gold standard were achieved. While in phase A a mean correlation of 0.98 and a root-mean-square error (RMSE) of 0.28 bpm was reached, in phase B the mean correlation and the RMSE hovered around 0.95 and 3.45 bpm, respectively. The higher RMSE in phase B results predominantly from delays between IRT and gold standard in BR transitions: eupnea/apnea, apnea/tachypnea etc. Moreover, this study also demonstrates the capability of IRT to capture varied breathing disorders, and consecutively, to assess respiratory function. In summary, IRT might be a promising monitoring alternative to the conventional contact-based techniques regarding its performance and remarkable capabilities.

Contactless monitoring of heart and respiratory rate in anesthetized pigs using infrared thermography.

Pig experiments have played an important role in medical breakthroughs during the last century. In fact, pigs are one of the major animal species used in translational research, surgical models and procedural training due to their anatomical and physiological similarities to humans. To ensure high bioethical standards in animal trials, new directives have been implemented, among others, to refine the procedures and minimize animals' stress and pain. This paper presents a contactless motion-based approach for monitoring cardiorespiratory signals (heart rate and respiratory rate) in anesthetized pigs using infrared thermography. Heart rate monitoring is estimated by measuring the vibrations (precordial motion) of the chest caused by the heartbeat. Respiratory rate, in turn, is computed by measuring the mechanical chest movements that accompany the respiratory cycle. To test the feasibility of this approach, thermal videos of 17 anesthetized pigs were acquired and analyzed. A high agreement between infrared thermography and a gold standard (electrocardiography and capnography-derived respiratory rate) was achieved. The mean absolute error averaged 3.43 ± 3.05 bpm and 0.27 ± 0.48 breaths/min for heart rate and respiratory rate, respectively. In sum, infrared thermography is capable of assessing cardiorespiratory signals in pigs. Future work should be conducted to evaluate infared thermography capability of capturing information for long term monitoring of research animals in a diverse set of facilities.

Remote vitals monitoring in rodents using video recordings.

Laboratory animal research was always crucial for scientific breakthroughs in the fields of medicine and biology. Animal trials offer insights into various disease mechanisms, genetics, drug therapy and the effect of different external factors onto living organisms. However, conducting animal trials is highly controversial. To ensure high ethical standards, a number of directives have been adopted in the European Union, which seek to replace, reduce and refine animal trials. Hence, severity assessment plays an important role in today's laboratory animal research. Currently, severity of trials is assessed by highly rater dependent scoring systems. In this paper, we propose a method for unobtrusive, automated and contactless measurement of respiratory rate (RR) and heart rate (HR). We were able to extract RR and HR with an high agreement between our method and a contact-based reference method. The Root Mean Squared Error (RMSE) averaged 0.32 ± 0.11 breaths/min for RR and 1.28 ± 0.62 beats/min for HR in rats, respectively. In mice, the RMSE averaged 1.42 ± 0.97 breaths/min for RR and 1.36 ± 0.87 beats/min, respectively. In the future, these parameters can be used for new, objective scoring systems, which are not susceptible to inter-rater variability.