Breath Detection Research Articles

TR-BREATH: Time-Reversal Breathing Rate Estimation and Detection.

In this paper, we introduce TR-BREATH, a time-reversal (TR)-based contact-free breathing monitoring system. It is capable of breathing detection and multiperson breathing rate estimation within a short period of time using off-the-shelf WiFi devices. The proposed system exploits the channel state information (CSI) to capture the miniature variations in the environment caused by breathing. To magnify the CSI variations, TR-BREATH projects CSIs into the TR resonating strength (TRRS) feature space and analyzes the TRRS by the Root-MUSIC and affinity propagation algorithms. Extensive experiment results indoor demonstrate a perfect detection rate of breathing. With only 10s of measurement, a mean accuracy of can be obtained for single-person breathing rate estimation under the non-line-of-sight (NLOS) scenario. Furthermore, it achieves a mean accuracy of in breathing rate estimation for a dozen people under the line-of-sight scenario and a mean accuracy of in breathing rate estimation of nine people under the NLOS scenario, both with 63s of measurement. Moreover, TR-BREATH can estimate the number of people with an error around 1. We also demonstrate that TR-BREATH is robust against packet loss and motions. With the prevailing of WiFi, TR-BREATH can be applied for in-home and real-time breathing monitoring.

Passive in-vehicle driver breath alcohol detection using advanced sensor signal acquisition and fusion

ABSTRACTObjective: The research objective of the present investigation is to demonstrate the present status of passive in-vehicle driver breath alcohol detection and highlight the necessary conditions for large-scale implementation of such a system. Completely passive detection has remained a challenge mainly because of the requirements on signal resolution combined with the constraints of vehicle integration. The work is part of the Driver Alcohol Detection System for Safety (DADSS) program aiming at massive deployment of alcohol sensing systems that could potentially save thousands of American lives annually.Method: The work reported here builds on earlier investigations, in which it has been shown that detection of alcohol vapor in the proximity of a human subject may be traced to that subject by means of simultaneous recording of carbon dioxide (CO2) at the same location. Sensors based on infrared spectroscopy were developed to detect and quantify low concentrations of alcohol and CO2. In the present investigation, alcohol and CO2 were recorded at various locations in a vehicle cabin while human subjects were performing normal in-step procedures and driving preparations. A video camera directed to the driver position was recording images of the driver's upper body parts, including the face, and the images were analyzed with respect to features of significance to the breathing behavior and breath detection, such as mouth opening and head direction.Results: Improvement of the sensor system with respect to signal resolution including algorithm and software development, and fusion of the sensor and camera signals was successfully implemented and tested before starting the human study. In addition, experimental tests and simulations were performed with the purpose of connecting human subject data with repeatable experimental conditions. The results include occurrence statistics of detected breaths by signal peaks of CO2 and alcohol. From the statistical data, the accuracy of breath alcohol estimation and timing related to initial driver routines (door opening, taking a seat, door closure, buckling up, etc.) can be estimated.The investigation confirmed the feasibility of passive driver breath alcohol detection using our present system. Trade-offs between timing and sensor signal resolution requirements will become critical. Further improvement of sensor resolution and system ruggedness is required before the results can be industrialized.Conclusions: It is concluded that a further important step toward completely passive detection of driver breath alcohol has been taken. If required, the sniffer function with alcohol detection capability can be combined with a subsequent highly accurate breath test to confirm the driver's legal status using the same sensor device. The study is relevant to crash avoidance, in particular driver monitoring systems and driver–vehicle interface design.

口臭検査装置の新規開発(第487回 大阪歯科学会例会 抄録)

口臭検査装置の新規開発(第487回 大阪歯科学会例会 抄録)

A flexible, ultra-sensitive strain sensor based on carbon nanocoil network fabricated by an electrophoretic method.

An ultra-sensitive strain sensor has been fabricated and studied, whose sensing medium is a network structure composed of plenty of carbon nanocoils (CNCs) and deposited between two gold electrodes via electrophoresis. The sensor owns a gauge factor close to 10 000 for tensile strain. A high stability and reproducibility of more than 5000 cycles and a fast response time of approximately 50 ms have been achieved. It has a high sensitivity for bending strain as well. Practically, it has shown an excellent performance in the detection of breathing, wrist pulse, and vibration. The sensor can be fabricated with a facile technology and low cost, suggesting great potential applications in micro-nano electromechanical systems, wearable devices and electronic skins.

Detection of exercise periodic breathing using thermal flowmeter in patients with heart failure.

Exercise periodic breathing (EPB) is associated with exercise intolerance and poor prognosis in patients with heart failure (HF). However, EPB detection during cardiopulmonary exercise test (CPET) is difficult. The present study investigated the use of a wireless monitoring device to record the EPB during CPET and proposed quantization parameter estimates for the EPB. A total of 445 patients with HF were enrolled and underwent exercise tests. The ventilation data from the wearable device were compared with the data obtained during the CPET and were analyzed based on professional opinion and on 2 automated programs (decision tree [DT] and oscillatory pattern methods). The measurement accuracy was greater with the DT method (89%) than with the oscillatory pattern method (75%). The cutoffs for EPB recognition using the DT method were (1) an intercept of the regression line passing through the minute ventilation rate vs. the time curve during the recovery phase ≥64.63, and (2) an oscillatory phase duration to total exercise time ratio ≥0.5828. The wearable device was suitable for the assessment of EPB in patients with HF, and our new automated analysis system using the DT method effectively identified the EPB pattern.

A proportional assist ventilator to unload respiratory muscles experimentally during exercise in humans.

What is the central question of this study? Can a modern proportional assist ventilator (PAV) function sufficiently well to unload the respiratory muscles during exercise? What is the main finding and its importance? A PAV can be constructed with contemporary hardware and software and be used at all exercise intensities to unload the respiratory muscles by up to 70%. Previously, PAVs have allowed researchers to address many fundamental physiological problems in clinical and healthy populations, but those versions are no longer functional or available. We describe the creation of a PAV that permits researchers to use it as an experimental tool. Manipulation of the normally occurring work of breathing (WOB) during exercise can provide insights into whole-body regulatory mechanisms in clinical patients and healthy subjects. One method to reduce the WOB uses a proportional assist ventilator (PAV). Suitable commercially available units are not capable of being used during heavy exercise. This investigation was undertaken in order to create a PAV and assess the degree to which the WOB could be reduced during exercise. A PAV works by creating a positive mouth pressure (Pm ) during inspiration, which consequently reduces the WOB. Spontaneous breathing patterns can be maintained, and the amplitude of Pm is calculated using the equation of motion and predetermined proportionality constants. We generated positive Pm using a breathing apparatus consisting of rigid tubing, solenoid valves to control the airflow direction and a proportional valve connected to compressed gas. Healthy male and female subjects were able to use the PAV successfully while performing cycling exercise over a range of intensities (50-100% of maximal workload) for different durations (from 30s to 20min) and different protocols (constant versus progressive workload). Inspiratory WOB was reduced up to 90%, while total WOB was reduced by 70%. The greatest reduction in WOB (50-75%) occurred during submaximal exercise, but at maximal ventilations (>180lmin(-1) ) a 50% reduction was still possible. The calculated change in WOB and subsequent reduction in respiratory muscle oxygen consumption resulted in equivalent reductions in whole-body oxygen consumption. With adequate familiarization and practice, our PAV can consistently reduce the WOB across a range of exercise intensities.

Detection of mixed volatile organic compounds and lung cancer breaths using chemiresistor arrays with crosslinked nanoparticle thin films

The ability to create sensing thin films for chemiresistors using crosslinked nanoparticle thin films with subtle structural differences in terms of interparticle linker molecular structure, nanoparticle composition and size is important not only for tuning sensitivity and selectivity in constructing a sensor array but also for enhancing stability of the sensors under ambient sensing conditions. In this report, we show that arrays of chemiresistors with such subtle structural differences are viable for detecting mixed volatile organic compounds (VOCs) and breath biomarkers under ambient conditions. The sensor arrays exhibit nanostructure-tunable sensitivity to VOCs and mixtures, with a limit of detection as low as 20ppb easily achievable for acetone. Preliminary testing of the sensor array in detecting breath samples from limited lung cancer patients, which consists of certain mixed VOCs as biomarkers, has also demonstrated the capability of breath recognition of lung cancer patients from healthy individuals under ambient sensing conditions. The recognition statistics were analyzed, showing the potential viability of achieving the desired sensitivity, selectivity, and accuracy in the breath sensing, the implication of which is discussed in terms of optimization of the sensor arrays for potential lung cancer screening.

Breathing and Snoring Sound Characteristics during Sleep in Adults.

Sound level meter is the gold standard approach for snoring evaluation. Using this approach, it was established that snoring intensity (in dB) is higher for men and is associated with increased apnea-hypopnea index (AHI). In this study, we performed a systematic analysis of breathing and snoring sound characteristics using an algorithm designed to detect and analyze breathing and snoring sounds. The effect of sex, sleep stages, and AHI on snoring characteristics was explored. We consecutively recruited 121 subjects referred for diagnosis of obstructive sleep apnea. A whole night audio signal was recorded using noncontact ambient microphone during polysomnography. A large number (> 290,000) of breathing and snoring (> 50 dB) events were analyzed. Breathing sound events were detected using a signal-processing algorithm that discriminates between breathing and nonbreathing (noise events) sounds. Snoring index (events/h, SI) was 23% higher for men (p = 0.04), and in both sexes SI gradually declined by 50% across sleep time (p < 0.01) independent of AHI. SI was higher in slow wave sleep (p < 0.03) compared to S2 and rapid eye movement sleep; men have higher SI in all sleep stages than women (p < 0.05). Snoring intensity was similar in both genders in all sleep stages and independent of AHI. For both sexes, no correlation was found between AHI and snoring intensity (r = 0.1, p = 0.291). This audio analysis approach enables systematic detection and analysis of breathing and snoring sounds from a full night recording. Snoring intensity is similar in both sexes and was not affected by AHI.

A flexible proximity sensor formed by duplex screen/screen-offset printing and its application to non-contact detection of human breathing.

We describe a flexible capacitance-type sensor that can detect an approaching human without contact, fabricated by developing and applying duplex conductive-ink printing to a film substrate. The results of our calculations show that the difference in size between the top and bottom electrodes of the sensor allows for the spatial extension of the electric field distribution over the electrodes. Hence, such a component functions as a proximity sensor. This thin and light device with a large form factor can be arranged at various places, including curved surfaces and the back of objects such that it is unnoticeable. In our experiment, we attached it to the back of a bed, and found that our device successfully detected the breathing of a subject on the bed without contacting his body. This should contribute to reducing the physical and psychological discomfort among patients during medical checks, or when their condition is being monitored.

Integrated micro power frequency breath detector

Monitoring vital signs, which include breathing, is often used both in clinical practice and at home. The paper presents a novel low power system that can be used to control breathing frequency and detect apnea, which uses a condenser-type humidity sensor supplied with input bias current of an operational amplifier. The new configuration of the sensor, which compensates for the amplifier saturation state during the exhalation phase, increases breath detection effectiveness.The presented system is energy efficient, consumes approx. 50μA and operates over a wide range of supply voltages, 1.8÷18V DC. It enables to detect even very weak breathing. The concept of the integrated system has also been presented. This system can work directly with digital circuits without converting the electric signal. The presented system is characterized by a very simple construction and offers a new solution both in the field of sensor technology and for the use of operational amplifiers.

見守りシステムのための移動ロボットによる呼吸推定方法の検討

見守りシステムのための移動ロボットによる呼吸推定方法の検討

Basic feasibility of breathing and pulse detection with a continuous wave Doppler radar sensor

s / Resuscitation 96S (2015) 43–157 53 not better in comparison with other causes of CA. These data suggest that further efforts are needed to prevent intubation-related CA. http://dx.doi.org/10.1016/j.resuscitation.2015.09.122

Development of a new homecare sleep monitor using body sounds and motion tracking

Abstract This paper presents the development of a sleep monitor to provide a comfortable way of detecting sleep-related breathing disorders like the obstructive sleep apnea syndrome (OSAS). OSAS is traditionally diagnosed using polysomnography, which requires a whole night stay at the sleep laboratory of a hospital with multiple electrodes and sensors attached to the patient’s body. However, body sound and motion tracking also provide extensive information about sleep course. A unique recording device offering a good body sound extraction, noise suppression and a small size is developed. Using this device a reliable detection of breathing and heart beat is possible. In addition sleeping positions and the activity of the patient will be evaluated using an inertial measurement unit (IMU). The device is easy to set up and offers the possibility to use it independently at home. Initial experiments have shown that volunteers were able to set up the device on their own. Furthermore several overnight recordings revealed the capability to monitor breathing, heart rate, sleeping position as well as movements of the patient.

Probabilistic cardiac and respiratory based classification of sleep and apneic events in subjects with sleep apnea

Current clinical standards to assess sleep and its disorders lack either accuracy or user-friendliness. They are therefore difficult to use in cost-effective population-wide screening or long-term objective follow-up after diagnosis. In order to fill this gap, the use of cardiac and respiratory information was evaluated for discrimination between different sleep stages, and for detection of apneic breathing. Alternative probabilistic visual representations were also presented, referred to as the hypnocorrogram and apneacorrogram. Analysis was performed on the UCD sleep apnea database, available on Physionet. The presence of apneic events proved to have a significant impact on the performance of a cardiac and respiratory based algorithm for sleep stage classification. WAKE versus SLEEP discrimination resulted in a kappa value of , while REM versus NREM resulted in and light sleep (N1N2) versus deep sleep (N3) in . The high proportion of hypopneic events led to poor detection of apneic breathing, resulting in a kappa value of . While the probabilistic representations allow to put classifier output in perspective, further improvements would be necessary to make the classifier reliable for use on patients with sleep apnea.

Efficient snoring and breathing detection based on sub-band spectral statistics.

Snoring, a common symptom in the general population may indicate the presence of obstructive sleep apnea (OSA). In order to detect snoring events in sleep sound recordings, a novel method was proposed in this paper. The proposed method operates by analyzing the acoustic characteristics of the snoring sounds. Based on these acoustic properties, the feature vectors are obtained using the mean and standard deviation of the sub-band spectral energy. A support vector machine is then applied to perform the frame-based classification procedure. This method was demonstrated experimentally to be effective for snoring detection. The database for detection included full-night audio recordings from four individuals who acknowledged having snoring habits. The performance of the proposed method was evaluated by classifying different events (snoring, breathing and silence) from the sleep sound recordings and comparing the classification against ground truth. The proposed algorithm was able to achieve an accuracy of 99.61% for detecting snoring events, 99.16% for breathing, and 99.55% for silence.

Preparation and Gas Sensing Properties of In2O3/Au Nanorods for Detection of Volatile Organic Compounds in Exhaled Breath.

A series of In2O3/Au nanorods (NRs) were fabricated and characterized by scanning electron microscope (SEM), transmission electron microscope (TEM), X–ray diffractometer (XRD) and X–ray photoelectron spectroscopy (XPS). The length to diameter ratios of In2O3/Au NRs was periodically modulated in the range of 2.9–4.5 through controlling the initial content of indium salt and reaction time. Their gas sensing properties to volatile organic compounds (VOCs) were carefully studied and then applied in exhaled breath detection. The results demonstrate that In2O3/Au NRs gas sensor can effectively detect acetone at 250 °C and ethanol at 400 °C. The corresponding actual detection limit is as low as 0.1 ppm to acetone and 0.05 ppm to ethanol, respectively. Moreover, by using humidity compensation method, In2O3/Au NRs gas sensor can clearly distinguish the acetone and ethanol biomarkers in human breath. The main reason of the enhanced gas sensing properties was attributed to the “spillover effects” between Au and In2O3 NRs. The excellent sensing performance indicates that In2O3/Au NRs is a promising functional material to actual application in monitoring and detecting diabetes and safe driving area in a noninvasive and more accurate way.

Use of the Nijmegen Questionnaire in asthma.

We may treat the Nijmegen Questionnaire score as a continuous variable rather than as a dichotomous tool http://ow.ly/OuOX5

EAMI: a qualitative quantification of periodic breathing based on amplitude of oscillations.

Periodic breathing is sleep disordered breathing characterized by instability in the respiratory pattern that exhibits an oscillatory behavior. Periodic breathing is associated with increased mortality, and it is observed in a variety of situations, such as acute hypoxia, chronic heart failure, and damage to respiratory centers. The standard quantification for the diagnosis of sleep related breathing disorders is the apnea-hypopnea index (AHI), which measures the proportion of apneic/ hypopneic events during polysomnography. Determining the AHI is labor-intensive and requires the simultaneous recording of airflow and oxygen saturation. In this paper, we propose an automated, simple, and novel methodology for the detection and qualification of periodic breathing: the estimated amplitude modulation index (eAMI). Antarctic Cohort (3800 meters): 13 normal individuals. Sleep Clinic Cohort: 39 different patients suffering from diverse sleep-related pathologies. When tested in a population with high levels of periodic breathing (Antarctic Cohort), eAMI was closely correlated with AHI (r = 0.95, P < 0.001). When tested in the clinical setting, the proposed method was able to detect portions of the signal in which subclinical periodic breathing was validated by an expert (n = 93; accuracy = 0.85). Average eAMI was also correlated with the loop gain for the combined clinical and Antarctica cohorts (r = 0.58, P < 0.001). In terms of quantification and temporal resolution, the eAMI is able to estimate the strength of periodic breathing and the underlying loop gain at any given time within a record. The impaired prognosis associated with periodic breathing makes its automated detection and early diagnosis of clinical relevance.

Terahertz volatile gas sensing by using polymer microporous membranes.

A compact, inexpensive, low loss, highly sensitive gas sensor is important for various biomedical and industrial applications. However, current gas sensors still have an inadequate study in terahertz (THz) frequency range. In this study, simple multilayer-stacked microporous polymer membranes are experimentally validated in the THz regime for organic vapor sensing under ambient atmosphere and room temperature. The hydrophilic porous polymer structure provides a large surface area to adsorb polar vapors, and exhibits excellent discrimination in different types of organic vapors based on distinct dipole moments. Various concentrations of volatile vapors can also be successfully distinguished by detecting the limits of low ppm concentrations. Furthermore, the microporous structural gas sensor has a reasonable response time in repeat usage. This study would provide new perspectives on toxic gas sensing and exhaled breath detection applications in the THz spectral frequency.

Review article: breath analysis in inflammatory bowel diseases.

There is an urgent need for cheap, reproducible, easy to perform and specific biomarkers for diagnosis, differentiation and stratification of inflammatory bowel disease (IBD) patients. Technical advances allow for the determination of volatile organic compounds in the human breath to differentiate between health and disease. Review and discuss medical literature on volatile organic compounds in exhaled human breath in GI disorders, focusing on diagnosis and differentiation of IBD. A systematic search in PubMed, Ovid Medline and Scopus was completed using appropriate keywords. In addition, a bibliography search of each article was performed. Mean breath pentane, ethane, propane, 1-octene, 3-methylhexane, 1-decene and NO levels were elevated (P < 0.05 to P < 10(-7)) and mean breath 1-nonene, (E)-2-nonene, hydrogen sulphide and methane were decreased in IBD compared to healthy controls (P = 0.003 to P < 0.001). A combined panel of 3 volatile organic compounds (octene, (E)-2-nonene and decene) showed the best discrimination between paediatric IBD and controls (AUC 0.96). Breath condensate cytokines were higher in IBD compared to healthy individuals (P < 0.008). Breath pentane, ethane, propane, isoprene and NO levels correlated with disease activity in IBD patients. Breath condensate interleukin-1β showed an inverse relation with clinical disease activity. Breath analysis in IBD is a promising approach that is not yet ready for routine clinical use, but data from other gastrointestinal diseases suggest the feasibility for use of this technology in clinical practice. Well-designed future trials, incorporating the latest breath detection techniques, need to determine the exact breath metabolome pattern linked to diagnosis and phenotype of IBD.