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.

Development of a low-cost, portable, pediatric infection screening system using simultaneous measurement of multiple vital signs.

The primary cause of death among children under age 5 years is acute respiratory infection, such as pneumonia. Detection of infection at the earliest point of contagion is necessary, to reduce morbidity and prevent infectious disease epidemics; therefore, identifying abnormal vital signs is essential. For early detection of pediatric infections, we developed a low-cost, portable, rapid screening system of pediatric infection. The system simultaneously measures three vital signs: heart rate (HR), respiration rate (RR), and body temperature (Temp) within 10 seconds using a pulse sensor, Doppler radar, and an infrared thermopile. Vital sign signal processing and computation are conducted using an Arduino Nano microprocessor, enabling the small, lightweight, and portable design of this system. Moreover, the cost-effectiveness of the system facilitates system applications in developing countries, which have the highest levels of pediatric mortality. We conducted trial measurement in Bayangol Health Center, Ulaanbaatar, Mongolia in 2019. A total of 50 children (age 1-14 years, 26 boys/24 girls) were enrolled in this study. Bland-Altman plot and Pearson correlation methods were used to evaluate the accuracy of the proposed system. The correlation coefficients were calculated as HR: r=0.92, RR: r=0.8, and Temp: r=0.82, with p<; 0.01. The system appears promising for rapid and convenient detection of infection in children.

Clinical Application of Multiple Vital Signs-Based Infection Screening System in a Mongolian Hospital: Optimization of Facial Temperature Measurement by Thermography at Various Ambient Temperature Conditions Using Linear Regression Analysis.

Fever is one significant sign of infection. Hence, infrared thermography systems are important for detecting infected suspects in public places. Reliable temperature measurements by thermography are influenced by several factors, including environmental conditions. This paper proposes a linear regression analysis-based facial temperature optimization method to improve the accuracy of multiple vital signs-based infection screening at various ambient temperatures. To obtain the relationship between ambient temperature and thermography measurements, 20 instances of axillary temperature, thermography measurements of facial temperature, and five different ambient temperature values at the time of measurement were used as a training set for a linear regression model. Temperatures from a total of 30 subjects were recalculated by the model. The screening system was evaluated using the temperature both before and after optimization to demonstrate the accuracy of the optimization method. A k-nearest neighbor algorithm was used to classify potentially infected patients from healthy subjects. Although the system has already been evaluated in restricted environmental conditions, this is the first time it was tested in Ulaanbaatar, Mongolia. The results show that the Pearson's correlation coefficient between optimum and axillary temperatures increased to r = 0.82. Paired t-tests revealed that the optimized temperature became statistically highly significant (p<0.001) for differentiating potentially infected patients from healthy subjects. Finally, the system achieved a sensitivity score of 91% and a negative predictive value of 92%. These values are higher than those obtained without temperature optimization. The proposed optimization method is feasible and can notably improve screening performance.

Development and Clinical Application of a Novel Autonomic Transient Response-Based Screening System for Major Depressive Disorder Using a Fingertip Photoplethysmographic Sensor.

Over 350 million people across the world suffer from major depressive disorder (MDD). More than 10% of MDD patients have suicide intent, while it has been reported that more than 40% patients did not consult their doctors for MDD. In order to increase consultation rate of potential MDD patients, we developed a novel MDD screening system which can be used at home without help of health-care professionals. Using a fingertip photoplethysmograph (PPG) sensor as a substitute of electrocardiograph (ECG), the system discriminates MDD patients from healthy subjects using autonomic nerve transient responses induced by a mental task (random number generation) via logistic regression analysis. The nine logistic regression variables are averages of heart rate (HR), high frequency (HF) component of heart rate variability (HRV), and the low frequency (LF)/HF ratio of HRV before, during, and after the mental task. We conducted a clinical test of the proposed system. Participants were 6 MDD patients (4 females and 2 males, aged 23-60 years) from Shizuoka Saiseikai General Hospital psychiatry outpatient unit and 14 healthy volunteers from University of Electro-Communications (6 females and 8 males, aged 21-63 years). The average PPG- and ECG (as a reference)-derived HR, HF and LF/HF were significantly correlated with each other (HR; r = 1.00, p < 0.0001, HF; r = 0.98, p < 0.0001, LF/HF; r = 0.98, p < 0.0001). Leave-one-out cross validation (LOOCV) revealed 83% sensitivity and 93% specificity. The proposed system appears promising for future MDD self-screening at home and are expected to encourage psychiatric visits for potential MDD patients.

Remote sensing of multiple vital signs using a CMOS camera-equipped infrared thermography system and its clinical application in rapidly screening patients with suspected infectious diseases.

BACKGROUND: Infrared thermography (IRT) is used to screen febrile passengers at international airports, but it suffers from low sensitivity. This study explored the application of a combined visible and thermal image processing approach that uses a CMOS camera equipped with IRT to remotely sense multiple vital signs and screen patients with suspected infectious diseases. METHODS: An IRT system that produced visible and thermal images was used for image acquisition. The subjects' respiration rates were measured by monitoring temperature changes around the nasal areas on thermal images; facial skin temperatures were measured simultaneously. Facial blood circulation causes tiny color changes in visible facial images that enable the determination of the heart rate. A logistic regression discriminant function predicted the likelihood of infection within 10s, based on the measured vital signs. Sixteen patients with an influenza-like illness and 22 control subjects participated in a clinical test at a clinic in Fukushima, Japan. RESULTS: The vital-sign-based IRT screening system had a sensitivity of 87.5% and a negative predictive value of 91.7%; these values are higher than those of conventional fever-based screening approaches. CONCLUSIONS: Multiple vital-sign-based screening efficiently detected patients with suspected infectious diseases. It offers a promising alternative to conventional fever-based screening.