Dynamic model of integrated cardiovascular and respiratory systems

Lee, Rong Mao; Tsai, Nan-Chyuan

doi:10.1002/mma.2748

Cited by 6 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The O 2 consumption rate depends on the concentration of O 2 in the tissue according the following equation :

M_{O_{2}} = {\overset{M}{true}}_{O_{2}} \frac{c_{t, O_{2}}}{β + c_{t, O_{2}}},

where

{\overset{M}{true}}_{O_{2}}

is the maximum consumption rate for O 2 ,

M_{O_{2}}

is the effective rate of production of O 2 , and β defines the point where the effective O 2 consumption is equivalent to 50% of the imposed value. Experimental details are depicted in the succeeding discussions, and the parameters used for these simulations were adapted from the values reported in and are summarized in Table .…”

Section: Resultsmentioning

confidence: 99%

An integrated mathematical model of the cardiovascular and respiratory systems

Trenhago

Fernandes

Müller

et al. 2015

Numer Methods Biomed Eng

View full text Add to dashboard Cite

This study presents a lumped model for the human cardiorespiratory system. Specifically, we incorporate a sophisticated gas dissociation and transport system to a fully integrated cardiovascular and pulmonary model. The model provides physiologically consistent predictions in terms of hemodynamic variables such as pressure, flow rate, gas partial pressures, and pH. We perform numerical simulations to evaluate the behavior of the partial pressures of oxygen and carbon dioxide in different vascular and pulmonary compartments. For this, we design the rest condition with low oxygen requirements and carbon dioxide production and exercise conditions with high oxygen demand and carbon dioxide production. Furthermore, model sensitivity to more relevant model parameters is studied. Copyright © 2015 John Wiley & Sons, Ltd.

show abstract

“…The O 2 consumption rate depends on the concentration of O 2 in the tissue according the following equation :

M_{O_{2}} = {\overset{M}{true}}_{O_{2}} \frac{c_{t, O_{2}}}{β + c_{t, O_{2}}},

where

{\overset{M}{true}}_{O_{2}}

is the maximum consumption rate for O 2 ,

M_{O_{2}}

Section: Resultsmentioning

confidence: 99%

An integrated mathematical model of the cardiovascular and respiratory systems

Trenhago

Fernandes

Müller

et al. 2015

Numer Methods Biomed Eng

View full text Add to dashboard Cite

show abstract

“…Based on G (f) in the frequency domain, CRI are defined to measure the status of the cardiopulmonary resonance system by adopting the cardiopulmonary system circuit model [ 42 , 55 , 56 ] ( Figure 2 ).…”

Section: Methodsmentioning

confidence: 99%

Automatic Detection of the Cyclic Alternating Pattern of Sleep and Diagnosis of Sleep-Related Pathologies Based on Cardiopulmonary Resonance Indices

Cui

Huang

Wu³

2022

Sensors

View full text Add to dashboard Cite

The cyclic alternating pattern is the periodic electroencephalogram activity occurring during non-rapid eye movement sleep. It is a marker of sleep instability and is correlated with several sleep-related pathologies. Considering the connection between the human heart and brain, our study explores the feasibility of using cardiopulmonary features to automatically detect the cyclic alternating pattern of sleep and hence diagnose sleep-related pathologies. By statistically analyzing and comparing the cardiopulmonary characteristics of a healthy group and groups with sleep-related diseases, an automatic recognition scheme of the cyclic alternating pattern is proposed based on the cardiopulmonary resonance indices. Using the Hidden Markov and Random Forest, the scheme combines the variation and stability of measurements of the coupling state of the cardiopulmonary system during sleep. In this research, the F1 score of the sleep-wake classification reaches 92.0%. In terms of the cyclic alternating pattern, the average recognition rate of A-phase reaches 84.7% on the CAP Sleep Database of 108 cases of people. The F1 score of disease diagnosis is 87.8% for insomnia and 90.0% for narcolepsy.

show abstract

“…From the physiology point of view, circulation and respiration work together toward the same goal; to acquire sufficient oxygen and deliver it to all the cells in the body. 29 Therefore, the analysis combined cardiovascular and respiratory factors (cardiorespiratory factors).…”

Section: W= F X S W = Work (J) F = Force (N) S = Distance (M)mentioning

confidence: 99%

Physical fitness assessment for lower limb disability: rationale and design to develop a new formula

Ernandini

Pakasi

Santoso³

et al. 2022

Bali Med J.

View full text Add to dashboard Cite

Introduction: Injuries are a major medical problem in military populations. The physical fitness assessment is a series of tests that measure and monitor physical fitness levels. The existing physical fitness assessment and formula used by the Indonesian Military requires a running test that only applies to healthy soldiers. This study attempted to construct a formula for assessing physical fitness using tests that soldiers may perform with lower limb injury/disability. Method: The design of the study was an internal cross-sectional comparison. This study included 104 healthy, well-trained male normal soldiers (NS) and 50 injured or disabled soldiers (DS) who matched the inclusion criteria. The NS was required to perform a 12-minute running and wheeling test, a ball-throwing test, push-ups, and lunges. The DS was also required to perform the same tests except for the 12-minute running test and lunges. The analysis of the data consisted of four steps: constructing a conceptual framework, describing the characteristics of each group, identifying significant indicators using Partial Least Square-Structural Equation Modeling (PLS-SEM) to develop a physical fitness formula, and determining whether the formula is appropriate for assessing physical fitness in running and wheeling tests. Result: There is no significant difference in the characteristics of NS and DS (p>0.05); thus, the subjects in both groups are comparable. Physical fitness test has several major contributing factors: distance, test method, muscle strength, and age. There is no significant difference in the number of NS in each category between the running and wheeling tests (p>0.05). Conclusion: Physical fitness formula= {1.1 × (0.98 × distance*) + (0.97 × method value**)} – (0.093 × age) – (0.17 × muscle strength***) Note: *) Distance traveled during running or wheeling test (meter). **) The running test method value is entered as 1, and the wheeling test method value is entered as 2. ***) The value of wheeling muscle strength is obtained from the ball-throwing test. In contrast, the value of running muscle strength can be determined by multiplying the result of the ball-throwing test by 2. These findings may be used for assessing the physical fitness of normal soldiers or those with lower limb injury/disability.

show abstract

Dynamic model of integrated cardiovascular and respiratory systems

Cited by 6 publications

References 20 publications

An integrated mathematical model of the cardiovascular and respiratory systems

An integrated mathematical model of the cardiovascular and respiratory systems

Automatic Detection of the Cyclic Alternating Pattern of Sleep and Diagnosis of Sleep-Related Pathologies Based on Cardiopulmonary Resonance Indices

Physical fitness assessment for lower limb disability: rationale and design to develop a new formula

Contact Info

Product

Resources

About