Tidal Volume and Instantaneous Respiration Rate Estimation using a Volumetric Surrogate Signal Acquired via a Smartphone Camera

2017 ◽  
Vol 21 (3) ◽  
pp. 764-777 ◽  
Author(s):  
Bersain A. Reyes ◽  
Natasa Reljin ◽  
Youngsun Kong ◽  
Yunyoung Nam ◽  
Ki H. Chon
Keyword(s):  
Tidal Volume ◽  
Respiration Rate ◽  
Rate Estimation

scholarly journals Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1393
Author(s):  
Uduak Z. George ◽  
Kee S. Moon ◽  
Sung Q. Lee
Keyword(s):  
Tidal Volume ◽  
Respiration Rate ◽  
Spline Interpolation ◽  
Vital Signs ◽  
Area Under The Curve ◽  
Sensor Technology ◽  
Wearable Sensor ◽  
Lung Sound ◽  
Health Monitoring System ◽  
Electrocardiogram Ecg

Respiratory activity is an important vital sign of life that can indicate health status. Diseases such as bronchitis, emphysema, pneumonia and coronavirus cause respiratory disorders that affect the respiratory systems. Typically, the diagnosis of these diseases is facilitated by pulmonary auscultation using a stethoscope. We present a new attempt to develop a lightweight, comprehensive wearable sensor system to monitor respiration using a multi-sensor approach. We employed new wearable sensor technology using a novel integration of acoustics and biopotentials to monitor various vital signs on two volunteers. In this study, a new method to monitor lung function, such as respiration rate and tidal volume, is presented using the multi-sensor approach. Using the new sensor, we obtained lung sound, electrocardiogram (ECG), and electromyogram (EMG) measurements at the external intercostal muscles (EIM) and at the diaphragm during breathing cycles with 500 mL, 625 mL, 750 mL, 875 mL, and 1000 mL tidal volume. The tidal volumes were controlled with a spirometer. The duration of each breathing cycle was 8 s and was timed using a metronome. For each of the different tidal volumes, the EMG data was plotted against time and the area under the curve (AUC) was calculated. The AUC calculated from EMG data obtained at the diaphragm and EIM represent the expansion of the diaphragm and EIM respectively. AUC obtained from EMG data collected at the diaphragm had a lower variance between samples per tidal volume compared to those monitored at the EIM. Using cubic spline interpolation, we built a model for computing tidal volume from EMG data at the diaphragm. Our findings show that the new sensor can be used to measure respiration rate and variations thereof and holds potential to estimate tidal lung volume from EMG measurements obtained from the diaphragm.

scholarly journals Respiration Rate Estimation Based on Independent Component Analysis of Accelerometer Data: Pilot Single-Arm Intervention Study

10.2196/17803 ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. e17803
Author(s):  
JeeEun Lee ◽  
Sun K Yoo
Keyword(s):  
Respiration Rate ◽  
Random Noise ◽  
Harmonic Component ◽  
Independent Component ◽  
Rank Test ◽  
P Value ◽  
Accelerometer Data ◽  
Mobile Environment ◽  
Accelerometer Sensor ◽  
Rate Estimation

Background As the mobile environment has developed recently, there have been studies on continuous respiration monitoring. However, it is not easy for general users to access the sensors typically used to measure respiration. There is also random noise caused by various environmental variables when respiration is measured using noncontact methods in a mobile environment. Objective In this study, we aimed to estimate the respiration rate using an accelerometer sensor in a smartphone. Methods First, data were acquired from an accelerometer sensor by a smartphone, which can easily be accessed by the general public. Second, an independent component was extracted to calibrate the three-axis accelerometer. Lastly, the respiration rate was estimated using quefrency selection reflecting the harmonic component because respiration has regular patterns. Results From April 2018, we enrolled 30 male participants. When the independent component and quefrency selection were used to estimate the respiration rate, the correlation with respiration acquired from a chest belt was 0.7. The statistical results of the Wilcoxon signed-rank test were used to determine whether the differences in the respiration counts acquired from the chest belt and from the accelerometer sensor were significant. The P value of the difference in the respiration counts acquired from the two sensors was .27, which was not significant. This indicates that the number of respiration counts measured using the accelerometer sensor was not different from that measured using the chest belt. The Bland-Altman results indicated that the mean difference was 0.43, with less than one breath per minute, and that the respiration rate was at the 95% limits of agreement. Conclusions There was no relevant difference in the respiration rate measured using a chest belt and that measured using an accelerometer sensor. The accelerometer sensor approach could solve the problems related to the inconvenience of chest belt attachment and the settings. It could be used to detect sleep apnea through constant respiration rate estimation in an internet-of-things environment.

Wavelet Variance Maximization: A contactless respiration rate estimation method based on remote photoplethysmography

2021 ◽  
Vol 63 ◽  
pp. 102263
Author(s):  
Duncan Luguern ◽  
Richard Macwan ◽  
Yannick Benezeth ◽  
Virginie Moser ◽  
L. Andrea Dunbar ◽  
...  
Keyword(s):  
Respiration Rate ◽  
Estimation Method ◽  
Rate Estimation ◽  
Remote Photoplethysmography ◽  
Wavelet Variance

scholarly journals Insulin increases ventilation during euglycemia in humans

2018 ◽  
Vol 315 (1) ◽  
pp. R84-R89 ◽  
Author(s):  
Thales C. Barbosa ◽  
Jasdeep Kaur ◽  
Seth W. Holwerda ◽  
Colin N. Young ◽  
Timothy B. Curry ◽  
...  
Keyword(s):  
Tidal Volume ◽  
Respiration Rate ◽  
Animal Studies ◽  
Plasma Insulin ◽  
Minute Ventilation ◽  
Elevated Plasma ◽  
Euglycemic Clamp ◽  
Hyperinsulinemic Euglycemic Clamp ◽  
Young Subjects ◽  
First Time

Evidence from animal studies indicates that hyperinsulinemia, without changes in glucose, increases ventilation via a carotid body-mediated mechanism. However, whether insulin elevates ventilation in humans independently of changes in glucose remains unclear. Therefore, we tested the hypothesis that insulin increases ventilation in humans during a hyperinsulinemic-euglycemic clamp in which insulin was elevated to postprandial concentrations while glucose was maintained at fasting concentrations. First, in 16 healthy young men ( protocol 1), we retrospectively analyzed respiration rate and estimated tidal volume from a pneumobelt to calculate minute ventilation during a hyperinsulinemic-euglycemic clamp. In addition, for a direct assessment of minute ventilation during a hyperinsulinemic-euglycemic clamp, we retrospectively analyzed breath-by-breath respiration rate and tidal volume from inspired/expired gasses in an additional 23 healthy young subjects ( protocol 2). Clamp infusion elevated minute ventilation from baseline in both protocols ( protocol 1: +11.9 ± 4.6% baseline, P = 0.001; protocol 2: +9.5 ± 3.8% baseline, P = 0.020). In protocol 1, peak changes in both respiration rate (+13.9 ± 3.0% baseline, P < 0.001) and estimated tidal volume (+16.9 ± 4.1% baseline, P = 0.001) were higher than baseline during the clamp. In protocol 2, tidal volume primarily increased during the clamp (+9.7 ± 3.7% baseline, P = 0.016), as respiration rate did not change significantly (+0.2 ± 1.8% baseline, P = 0.889). Collectively, we demonstrate for the first time in humans that elevated plasma insulin increases minute ventilation independent of changes in glucose.

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