Pulse rate variability estimation method based on imaging-photoplethysmography and application to telepsychiatry.

The worldwide adoption of telehealth services may benefit people who otherwise would not be able to access mental health support. In this paper, we present a novel algorithm to obtain reliable pulse and respiration signals from non-contact facial image sequence analysis. The proposed algorithm involved a skin pixel extraction method in the image processing part and signal reconstruction using the spectral information of RGB signal in the signal processing part. The algorithm was tested on 15 healthy subjects in a laboratory setting. The results show that the proposed algorithm can accurately monitor respiration rate (RR), pulse rate (PR), and pulse rate variability (PRV) in rest conditions.Clinical Relevance- The main achievement of this study is enabling non-contact PR and RR signal extraction from facial image sequences, which has potential for future use and support for psychiatrists in telepsychiatry.

Remote sensing of vital signs by medical radar time-series signal using cardiac peak extraction and adaptive peak detection algorithm: Performance validation on healthy adults and application to neonatal monitoring at an NICU.

BACKGROUND AND OBJECTIVE: Continuous monitoring of vital signs plays a pivotal role in neonatal intensive care units (NICUs). In this paper, we present a system for monitoring fully non-contact medical radar-based vital signs to measure the respiratory rate (RR), heart rate (HR), I:E ratio, and heart rate variability (HRV). In addition, we evaluated its performance in a physiological laboratory and examined its adaptability in an NICU. METHODS: A non-contact medical radar-based vital sign monitoring system that includes 24 GHz radar installed in an incubator was developed. To enable reliable monitoring, an advanced signal processing algorithm (i.e., a nonlinear filter to separate respiration and heartbeat signals from the output of radar), template matching to extract cardiac peaks, and an adaptive peak detection algorithm to estimate cardiac peaks in time-series were proposed and implemented in the system. Nine healthy subjects comprising five males and four females (24 ± 5 years) participated in the laboratory test. To evaluate the adaptability of the system in an NICU setting, we tested it with three hospitalized infants, including two neonates. RESULTS: The results indicate strong agreement in healthy subjects between the non-contact system and reference contact devices for RR, HR, and inter-beat interval (IBI) measurement, with correlation coefficients of 0.83, 0.96, and 0.94, respectively. As anticipated, the template matching and adaptive peak detection algorithms outperformed the conventional approach. These showed a more accurate IBI close to the reference Bland-Altman analysis (proposed: bias of -3 ms, and 95% limits of agreement ranging from -73 to 67 ms; conventional: bias of -11 ms, and 95% limits of agreement ranging from -229 to 207 ms). Moreover, in the NICU clinical setting, the IBI correlation coefficient and 95% limit of agreement in the conventional method are 0.31 and 91 ms. The corresponding values obtained using the proposed method are 0.93 and 21 ms. CONCLUSION: The proposed system introduces a novel approach for NICU monitoring using a non-contact medical radar sensor. The signal processing method combining cardiac peak extraction algorithm with the adaptive peak detection algorithm shows high adaptability in detecting IBI the time series in various application settings.

An mHealth App for the Non-contact Measurement of Pulmonary Function Using the Smartphone's Built-in Depth Sensor.

The use of smartphones in clinical practice is referred to as mobile health (mHealth). This has attracted great interest in both academia and industry because of its potential to augment healthcare. In this study, we developed an mHealth app for the non-contact measurement of chest-wall movements using the iPhone ' s built-in depth sensor, thereby enabling a pulmonary self-monitoring function for personal use. The depth sensor provides depth values for each pixel and 2D mapping of the chest-wall movements. To extract respiratory signals from the right and left thoracic regions and abdomen, a 2D-depth image-segmentation method was implemented. The method was based on the anatomy and physiology of chest-wall movements, assuming differences in the anterior displacement in the thoracic and abdominal regions. It was observed that the differences were significant in the segmented regions of interest (ROIs) of the right and left thoracic region and abdomen. Respiratory signals extracted from each ROI were compared with the contact bio-impedance signals, which were highly correlated (r=0.94).

Proof-of-principle Experiment on 24 GHz Medical Radar for Non-contact Vital Signs Measurement.

Medical radar for non-contact vital signs measurement exhibits great potential in both clinical and home healthcare settings. Especially during the corona virus spreading time, non-contact sensing more clearly shows the advantages. Many previous studies have concentrated on medical radar-based healthcare applications, but pay less attention to the working principles. A clear understanding of medical radars at both the mathematical and physical levels is critically important for developing application-specific signal processing algorithms. Therefore, this study aims to re-define the operating principle of radar, and a proof-of-principle experiment was performed on both actuator and human subjects using 24 GHz Doppler radar system. Experimental results indicate that there is a difference in the radar output signals between the two cases, where the displacement is greater than and less than half of the wavelength. For the former situation, the displacement x = n.λ/2 (n ≥ 1), one peak of radar signals corresponds to n peaks of baseband signals. By contrast, for the latter situation, the displacement x < λ/2, one peak of radar signals corresponds to one peak of baseband signals. Strikingly, with human measurement on the dorsal side, the the number of respiration peaks are seen from the radar raw signals.

Design and Evaluation of Digital Filters for Non-Contact Measuring of HRV using Medical Radar and Its Application in Bedside Patient Monitoring System.

A non-contact bedside monitoring system using medical radar is expected to be applied to clinical fields. Our previous studies have developed a monitoring system based on medical radar for measuring respiratory rate (RR) and heart rate (HR). Heart rate variability (HRV), which is essentially implemented in advanced monitoring system, such as prognosis prediction, is a more challenging biological information than the RR and HR. In this study, we designed a HRV measurement filter and proposed a method to evaluate the optimal cardiac signal extraction filter for HRV measurement. Because the cardiac component in the radar signal is much smaller than the respiratory component, it is necessary to extract the cardiac element from the radar output signal using digital filters. It depends on the characteristics of the filter whether the HRV information is kept in the extracted cardiac signal or not. A cardiac signal extraction filter that is not distorted in the time domain and does not miss the cardiac component must be adopted. Therefore, we focused on evaluating the interval between the R-peak of the electrocardiogram (ECG) and the radar-cardio peak of the cardiac signal measured by radar (R-radar interval). This is based on the fact that the time between heart depolarization and ventricular contraction is measured as the R-radar interval. A band-pass filter (BPF) with several bandwidths and a nonlinear filter, locally projective adaptive signal separation (LoPASS), were analyzed and compared. The optimal filter was quantitatively evaluated by analyzing the distribution and standard deviation of the R-radar intervals. The performance of this monitoring system was evaluated in elderly patient at the Yokohama Hospital, Japan.

Non-contact Measurement of Pulse Rate Variability Using a Webcam and Application to Mental Illness Screening System.

The COVID-19 pandemic is a global health crisis. Mental health is critical in such uncertain situations, particularly when people are required to significantly restrict their movements and change their lifestyles. Under these conditions, many countries have turned to telemedicine to strengthen and expand mental health services. Our research group previously developed a mental illness screening system based on heart rate variability (HRV) analysis, enabling an objective and easy mental health self-check. This screening system cannot be used for telemedicine because it uses electrocardiography (ECG) and contact photoplethysmography (PPG), that are not widely available outside of a clinical setting. The purpose of this study is to enable the extension of the aforementioned system to telemedicine by the application of non-contact PPG using an RGB webcam, also called imaging- photoplethysmography (iPPG). The iPPG measurement errors occur due to changes in the relative position between the camera and the target, and due to changes in light. Conventionally, in image processing, the pixel value of the entire face region is used. We propose skin pixel extraction to eliminate blinks, eye movements, and changes in light and shadow. In signal processing, the green channel signal is conventionally used as a pulse wave owing to the absorption characteristics of blood flow. Taking advantage of the fact that the red and blue channels contain noise, we propose a signal reconstruction method for removing noise and strengthening the signal in the pulse rate variability (PRV) frequency band by weighting the three signals of the RGB camera. We conducted an experiment with 13 healthy subjects, and showed that the PRV index and pulse rate (PR) errors estimated by the proposed method were smaller than those of the conventional method. The correlation coefficients between estimated values by the proposed method and reference values of LF, HF, and PR were 0.86, 0.69, and 0.96, respectively.

Contactless Vital Signs Measurement System Using RGB-Thermal Image Sensors and Its Clinical Screening Test on Patients with Seasonal Influenza.

Background: In the last two decades, infrared thermography (IRT) has been applied in quarantine stations for the screening of patients with suspected infectious disease. However, the fever-based screening procedure employing IRT suffers from low sensitivity, because monitoring body temperature alone is insufficient for detecting infected patients. To overcome the drawbacks of fever-based screening, this study aims to develop and evaluate a multiple vital sign (i.e., body temperature, heart rate and respiration rate) measurement system using RGB-thermal image sensors. Methods: The RGB camera measures blood volume pulse (BVP) through variations in the light absorption from human facial areas. IRT is used to estimate the respiration rate by measuring the change in temperature near the nostrils or mouth accompanying respiration. To enable a stable and reliable system, the following image and signal processing methods were proposed and implemented: (1) an RGB-thermal image fusion approach to achieve highly reliable facial region-of-interest tracking, (2) a heart rate estimation method including a tapered window for reducing noise caused by the face tracker, reconstruction of a BVP signal with three RGB channels to optimize a linear function, thereby improving the signal-to-noise ratio and multiple signal classification (MUSIC) algorithm for estimating the pseudo-spectrum from limited time-domain BVP signals within 15 s and (3) a respiration rate estimation method implementing nasal or oral breathing signal selection based on signal quality index for stable measurement and MUSIC algorithm for rapid measurement. We tested the system on 22 healthy subjects and 28 patients with seasonal influenza, using the support vector machine (SVM) classification method. Results: The body temperature, heart rate and respiration rate measured in a non-contact manner were highly similarity to those measured via contact-type reference devices (i.e., thermometer, ECG and respiration belt), with Pearson correlation coefficients of 0.71, 0.87 and 0.87, respectively. Moreover, the optimized SVM model with three vital signs yielded sensitivity and specificity values of 85.7% and 90.1%, respectively. Conclusion: For contactless vital sign measurement, the system achieved a performance similar to that of the reference devices. The multiple vital sign-based screening achieved higher sensitivity than fever-based screening. Thus, this system represents a promising alternative for further quarantine procedures to prevent the spread of infectious diseases.

Soluble αKlotho as a candidate for the biomarker of aging.

Although the Klotho gene has been recognized as an aging-suppressor gene, the significance of its soluble product, soluble αKlotho (sKlotho), in aging remains to be elucidated. To address this issue, we conducted a single-centered cross-sectional study in a region with a high prevalence of aging. We compared sKlotho levels with the patient characteristics from medical records and laboratory measurements, including fibroblast growth factor 23 (FGF23), intact parathyroid hormone, activated vitamin D3 and factors associated with mineral bone metabolism, in 52 outpatients with a mean age of 78.2 years. Serum sKlotho levels significantly decreased with age, but were not associated with the stage of chronic kidney disease (CKD). Serum FGF23 levels increased as CKD stages advanced, but were not associated with aging. Univariate analyses revealed that sKlotho levels positively correlated with glomerular filtration rate, and negatively with age and serum levels of FGF23 and phosphorus. In a multivariable linear regression analysis, sKlotho significantly correlated with aging and lower FGF23 levels. Only osteoporosis affected sKlotho and FGF23 levels among the various complications and patient status including medication. In summary, serum sKlotho levels inversely correlated with age and FGF23, and were significantly reduced in patients with osteoporosis. sKlotho may serve as a biomarker of aging independent of renal function.

3-(3-Phenoxybenzyl)amino-ß-carboline: a novel antitumor drug targeting α-tubulin.

3-(3-Phenoxybenzyl)amino-ß-carboline 2h showed extremely-high activity; the IC(50) value was 0.074 µM. To verify 2h-induced cell death types, we observed the chromatin condensation, the DNA fragmentation and activated caspase-3 using Hoechst 33342, agarose electrophoresis and western blot, and suggesting 2h-induced cell death type was apoptosis. Flow cytometry showed that 2h-treated cell was induced SubG1 cell population after G2/M cell cycle arrest. In addition, using affinity chromatography and peptide mass fingerprinting, we found that interacting protein with this compound was α-tubulin protein.

3-benzylamino-ß-carboline derivatives induce apoptosis through G2/M arrest in human carcinoma cells HeLa S-3.

ß-carboline derivatives are known as the lead compounds for anti-tumor agents. To examine an optimal structure for anti-tumor activity, we synthesized a variety of ß-carboline derivatives, possessing a variety of substituents on the nitrogen atom of the amino group of 3-amino-ß-carboline, and evaluated their anti-tumor activity for HeLa S-3 cell line. 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay showed that an optimal structure for anti-tumor activity was 3-cyclohexylmethylamino (1e) or 3-benzylamino-ß-carboline (1f). An optimal counter anion of 2-methyl-3-benzylamino- ß-carbolinium salts was a triflate anion 2c. In addition, the introduction of a hydroxyl group on the meta-position of the benzyl group of 3-benzylamino-ß-carboline (3e) enhanced its anti-tumor activity. Hoechst 33342 staining and DNA fragmentation assay suggested that 1f, 2c and 3e induced cell death by apoptosis unlike 1e. Flow cytometry analysis showed that 1f, 2c and 3e induced cell apoptosis through arrest of the cell cycle in the G2/M phase.