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Pulse-oximetry has become a core monitoring modality in most fields of medicine. Typical dual-wavelength pulse-oximeters estimate blood oxygen saturation (SpO2) levels from a relationship between the amplitudes of red and infrared photoplethysmographic (PPG) waveforms. When captured with a camera, the PPG waveforms are much weaker and consequently the measurement is more sensitive to distortions and noises. Therefore, an indirect method has recently been proposed where, instead of extracting the relative amplitudes from the individual waveforms, the waveforms are linearly combined to construct a collection of pulse signals with different pulse signatures, each corresponding to a specific oxygen saturation level. This method has been shown to outperform the conventional ratio-of-ratios based methods, especially when adding a third wavelength. Adding wavelengths, however, complicates the calibration. Inaccuracies in the calibration model threaten the performance of the method. Opto-physiological models have been shown earlier to provide useful calibration parameter estimates. In this paper, we show that the accuracy can be improved using a data-driven approach. We performed 5-fold cross validation on recordings with variations in oxygen saturation and optimized for pulse quality. All evaluated wavelength combinations, also without visible red, meet the required ISO standard accuracy with the calibration from the proposed method. This scalable approach is not only helpful to fine-tune the calibration model, but even allows computation of the calibration model parameters from scratch without prior knowledge of the data acquisition details, i.e., the properties of camera and illumination. [ABSTRACT FROM AUTHOR]

Low-resolution thermal cameras have already been used in the detection of respiratory flow. However, microbolometer technology has a high production cost compared to thermopile arrays. In this work, the feasibility of using a thermopile array to detect respiratory flow has been investigated in multiple settings. To prove the concept, we tested the detector on six healthy subjects. Our method automatically selects the region-of-interest by discriminating between sensor elements that output noise and flow-induced signals. The thermopile array yielded an average root mean squared error of 1.59 b r e a t h s p e r m i n u t e . Parameters such as distance, breathing rate, orientation, and oral or nasal breathing resulted in being fundamental in the detection of respiratory flow. The paper provides the proof-of-concept that low-cost thermopile-arrays can be used to monitor respiratory flow in a lab setting and without the need for facial landmark detection. Further development could provide a more attractive alternative for the earlier bolometer-based proposals. [ABSTRACT FROM AUTHOR]

Keywords: remote vital sign monitoring; camera-based photo-plethysmography; video health monitoring, artificial intelligence When video streams rather than static images are considered, not only is diagnosis but also patient monitoring in scope: a large number of vital signs can be measured with consumer level cameras. Moreover, heart and respiration rate may be measured with quite different technologies including capacitive ECG, radar, image-based motion, and rPPG, which makes benchmarking these technologies relevant. Measurement of SpO SB 2 sb is arguably more difficult than heart rate since the relative amplitudes of the rPPG signal need to be measured rather than the mere periodicities of rPPG signals. [Extracted from the article]

Both Respiratory Flow (RF) and Respiratory Motion (RM) are visible in thermal recordings of infants. Monitoring these two signals usually requires landmark detection for the selection of a region of interest. Other approaches combine respiratory signals coming from both RF and RM, obtaining a Mixed Respiratory (MR) signal. The detection and classification of apneas, particularly common in preterm infants with low birth weight, would benefit from monitoring both RF and RM, or MR, signals. Therefore, we propose in this work an automatic RF pixel detector not based on facial/body landmarks. The method is based on the property of RF pixels in thermal videos, which are in areas with a smooth circular gradient. We defined 5 features combined with the use of a bank of Gabor filters that together allow selection of the RF pixels. The algorithm was tested on thermal recordings of 9 infants amounting to a total of 132 min acquired in a neonatal ward. On average the percentage of correctly identified RF pixels was 84 % . Obstructive Apneas (OAs) were simulated as a proof of concept to prove the advantage in monitoring the RF signal compared to the MR signal. The sensitivity in the simulated OA detection improved for the RF signal reaching 73 % against the 23 % of the MR signal. Overall, the method yielded promising results, although the positioning and number of cameras used could be further optimized for optimal RF visibility. [ABSTRACT FROM AUTHOR]

Aiming at continuous unobtrusive respiration monitoring, motion robustness is paramount. However, some types of motion can completely hide the respiration information and the detection of these events is required to avoid incorrect rate estimations. Therefore, this work proposes a motion detector optimized to specifically detect severe motion of infants combined with a respiration rate detection strategy based on automatic pixels selection, which proved to be robust to motion of the infants involving head and limbs. A dataset including both thermal and RGB (Red Green Blue) videos was used amounting to a total of 43 h acquired on 17 infants. The method was successfully applied to both RGB and thermal videos and compared to the chest impedance signal. The Mean Absolute Error (MAE) in segments where some motion is present was 1.16 and 1.97 breaths/min higher than the MAE in the ideal moments where the infants were still for testing and validation set, respectively. Overall, the average MAE on the testing and validation set are 3.31 breaths/min and 5.36 breaths/min, using 64.00 % and 69.65 % of the included video segments (segments containing events such as interventions were excluded based on a manual annotation), respectively. Moreover, we highlight challenges that need to be overcome for continuous camera-based respiration monitoring. The method can be applied to different camera modalities, does not require skin visibility, and is robust to some motion of the infants. [ABSTRACT FROM AUTHOR]