Rapid and stable measurement of respiratory rate from Doppler radar signals using time domain autocorrelation model

Noncontact measurement of respiratory rate using Doppler radar will play a vital role in future clinical practice. Doppler radar remotely monitors the tiny chest wall movements induced by respiration activity. The most competitive advantage of this technique is to allow users fully unconstrained with no biological electrode attachments. However, the Doppler radar, unlike other contact-type sensors, is easily affected by the random body movements. In this paper, we proposed a time domain autocorrelation model to process the radar signals for rapid and stable estimation of the respiratory rate. We tested the autocorrelation model on 8 subjects in laboratory, and compared the respiratory rates detected by noncontact radar with reference contact-type respiratory effort belt. Autocorrelation model showed the effects of reducing the random body movement noise added to Doppler radar's respiration signals. Moreover, the respiratory rate can be rapidly calculated from the first main peak in the autocorrelation waveform within 10 s.

Design an easy-to-use infection screening system for non-contact monitoring of vital-signs to prevent the spread of pandemic diseases

The outbreak of infectious diseases such as influenza, dengue fever, and severe acute respiratory syndrome (SARS) are threatening the global health. Especially, developing countries in the South-East Asia region have been at serious risk. Rapid and highly reliable screening of infection is urgently needed during the epidemic season at mass gathering places, such as airport quarantine facilities, public health centers, and hospital outpatients units, etc. To meet this need, our research group is currently developing a multiple vital-signs based infection screening system that can perform human medical inspections within 15 seconds. This system remotely monitors facial temperature, heart and respiration rates using a thermopile array and a 24-GHz microwave radar, respectively. In this work, we redesigned our previous system to make a higher performance with a user-friendly interface. Moreover, the system newly included a multivariable logistic regression model (MLRM) to determine the possibility of infection. We tested the system on 34 seasonal influenza patients and 35 normal control subjects at the Japan Self-Defense Forces Central Hospital. The sensitivity and specificity of the screening system using the MLRM were 85.3% and 88.6%, respectively.

An infectious disease/fever screening radar system which stratifies higher-risk patients within ten seconds using a neural network and the fuzzy grouping method

Objectives

To classify higher-risk influenza patients within 10 s, we developed an infectious disease and fever screening radar system.

Methods

The system screens infected patients based on vital signs, i.e., respiration rate measured by a radar, heart rate by a finger-tip photo-reflector, and facial temperature by a thermography. The system segregates subjects into higher-risk influenza (HR-I) group, lower-risk influenza (LR-I) group, and non-influenza (Non-I) group using a neural network and fuzzy clustering method (FCM). We conducted influenza screening for 35 seasonal influenza patients and 48 normal control subjects at the Japan Self-Defense Force Central Hospital. Pulse oximetry oxygen saturation (SpO₂) was measured as a reference.

Results

The system classified 17 subjects into HR-I group, 26 into LR-I group, and 40 into Non-I group. Ten out of the 17 HR-I subjects indicated SpO₂ <96%, whereas only two out of the 26 LR-I subjects showed SpO₂ <96%. The chi-squared test revealed a significant difference in the ratio of subjects showed SpO₂ <96% between HR-I and LR-I group (p < 0.001). There were zero and nine normal control subjects in HR-I and LR-I groups, respectively, and there was one influenza patient in Non-I group.

Conclusions

The combination of neural network and FCM achieved efficient detection of higher-risk influenza patients who indicated SpO₂ 96% within 10 s.

•

A novel infectious disease/fever screening radar system stratifies higher-risk patients within ten seconds.

•

Use of an optimal neural network and the fuzzy clustering method to classify multiple-dimensional vital signs data.

•

The system can be used for preventing secondary exposure of physicians during outbreaks of infectious disease.

•

The system has potential to serve as a helpful tool for rapid mass screening of infectious disease.

Rapid screening for influenza using a multivariable logistic regression model to save labor at a clinic in Iwaki, Fukushima, Japan

To lighten the workload of health care professionals, we conducted a clinical test of a newly developed automated infection screening system using a multivariable logistic regression model. The system was tested with 44 influenza patients and 45 healthy control subjects based on 3 vital signs: facial temperature, heart rate and respiratory rate. The system showed a high accuracy for distinguishing influenza patients from control subjects within 15 seconds.