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Investigation of non-uniform airflow signal oscillation during high frequency chest compression.

Sohn K, Warwick WJ, Lee YW, Lee J, Holte JE - Biomed Eng Online (2005)

Bottom Line: The simulation results indicated that lung capacitance or the inertance of air is also not a factor in the non-uniformity of HFCC airflow signals.Although not perfect, our circuit analogue model allows us to effectively simulate the nonlinear characteristics of the respiratory system.We found that the amplitudes of HFCC airflow signals behave as a function of spontaneous airflow signals.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA. sohn0015@umn.edu

ABSTRACT

Background: High frequency chest compression (HFCC) is a useful and popular therapy for clearing bronchial airways of excessive or thicker mucus. Our observation of respiratory airflow of a subject during use of HFCC showed the airflow oscillation by HFCC was strongly influenced by the nonlinearity of the respiratory system. We used a computational model-based approach to analyse the respiratory airflow during use of HFCC.

Methods: The computational model, which is based on previous physiological studies and represented by an electrical circuit analogue, was used for simulation of in vivo protocol that shows the nonlinearity of the respiratory system. Besides, airflow was measured during use of HFCC. We compared the simulation results to either the measured data or the previous research, to understand and explain the observations.

Results and discussion: We could observe two important phenomena during respiration pertaining to the airflow signal oscillation generated by HFCC. The amplitudes of HFCC airflow signals varied depending on spontaneous airflow signals. We used the simulation results to investigate how the nonlinearity of airway resistance, lung capacitance, and inertance of air characterized the respiratory airflow. The simulation results indicated that lung capacitance or the inertance of air is also not a factor in the non-uniformity of HFCC airflow signals. Although not perfect, our circuit analogue model allows us to effectively simulate the nonlinear characteristics of the respiratory system.

Conclusion: We found that the amplitudes of HFCC airflow signals behave as a function of spontaneous airflow signals. This is due to the nonlinearity of the respiratory system, particularly variations in airway resistance.

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The airflow signals measured at the mouth of the subject, the high-pass filtered (HFCC airflow signal) and the low-pass filtered (spontaneous airflow signal) curves. The subject was using HFCC with (a) 5 Hz, (b) 15 Hz, and (c) 21 Hz. Regardless of the frequencies, larger spontaneous airflow signals result in smaller HFCC airflow signals. Since it is difficult to breathe hard during 21 Hz, spontaneous airflow signals in (c) are smaller than in (a) and (b). Phase I is the portion of the inspiration phase when spontaneous airflow signals are greater than the amplitudes of HFCC airflow signals. During phase II, spontaneous airflow signals stay within the amplitudes of HFCC airflow signals. In phase III, expiration begins, and the amplitudes of airflow oscillation decrease to about the amplitudes during phase I. Spontaneous airflow signals get greater to the negative direction than HFCC airflow signals. Finally phase IV, the resting period before inspiration, begins, and the amplitudes of HFCC airflow signals again become greater than spontaneous airflow signals. To emphasize the difference of HFCC airflow signals, phase II were consciously prolonged and they are longer than phase II in fig. 6.
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Figure 5: The airflow signals measured at the mouth of the subject, the high-pass filtered (HFCC airflow signal) and the low-pass filtered (spontaneous airflow signal) curves. The subject was using HFCC with (a) 5 Hz, (b) 15 Hz, and (c) 21 Hz. Regardless of the frequencies, larger spontaneous airflow signals result in smaller HFCC airflow signals. Since it is difficult to breathe hard during 21 Hz, spontaneous airflow signals in (c) are smaller than in (a) and (b). Phase I is the portion of the inspiration phase when spontaneous airflow signals are greater than the amplitudes of HFCC airflow signals. During phase II, spontaneous airflow signals stay within the amplitudes of HFCC airflow signals. In phase III, expiration begins, and the amplitudes of airflow oscillation decrease to about the amplitudes during phase I. Spontaneous airflow signals get greater to the negative direction than HFCC airflow signals. Finally phase IV, the resting period before inspiration, begins, and the amplitudes of HFCC airflow signals again become greater than spontaneous airflow signals. To emphasize the difference of HFCC airflow signals, phase II were consciously prolonged and they are longer than phase II in fig. 6.

Mentions: Fig. 4 is the airflow the oesophageal pressure measured by Fink et al. [6], which shows typical respiratory airflow during use of HFCC. Fig. 5 shows the subject's airflow signals during ten seconds at the three different frequencies. In this figure, each airflow signal is also viewed as the low-pass filtered and the high-pass filtered curves. The high-pass filtered curve indicates the fast airflow signal oscillation generated by HFCC pulses (HFCC airflow signal) whereas the low-pass filtered curve is the airflow during the spontaneous breathing effort (spontaneous airflow signal) of the subject. We divided one cycle of the respiration into four phases. Phase I is the portion of the inspiration phase when the spontaneous airflow signal is greater than the amplitudes of HFCC airflow signal. In this phase LV increases. During phase II, the spontaneous airflow signal stays within the amplitudes of the HFCC airflow signal. Phase II is the pause before expiration begins. The amplitudes of HFCC airflow signals change considerably as the phase moves from I to II. To emphasize the difference of HFCC airflow signals, phase II were consciously prolonged and they are longer than phase II in fig. 6. In phase III, passive or active expiration begins and the amplitudes of HFCC airflow signals decrease to about phase I amplitudes. The low-pass curve gets greater to the negative direction curve than the high-pass curve. The next and last phase is phase IV. Phase IV is the resting period before inspiration, begins. During this phase the amplitudes of HFCC airflow signals again become greater than the spontaneous airflow signal. To reproduce similar airflow signals, a cycle of respiration from phase IV to phase IV was simulated using our computational model. The simulation results with the three frequencies are shown in fig. 6.


Investigation of non-uniform airflow signal oscillation during high frequency chest compression.

Sohn K, Warwick WJ, Lee YW, Lee J, Holte JE - Biomed Eng Online (2005)

The airflow signals measured at the mouth of the subject, the high-pass filtered (HFCC airflow signal) and the low-pass filtered (spontaneous airflow signal) curves. The subject was using HFCC with (a) 5 Hz, (b) 15 Hz, and (c) 21 Hz. Regardless of the frequencies, larger spontaneous airflow signals result in smaller HFCC airflow signals. Since it is difficult to breathe hard during 21 Hz, spontaneous airflow signals in (c) are smaller than in (a) and (b). Phase I is the portion of the inspiration phase when spontaneous airflow signals are greater than the amplitudes of HFCC airflow signals. During phase II, spontaneous airflow signals stay within the amplitudes of HFCC airflow signals. In phase III, expiration begins, and the amplitudes of airflow oscillation decrease to about the amplitudes during phase I. Spontaneous airflow signals get greater to the negative direction than HFCC airflow signals. Finally phase IV, the resting period before inspiration, begins, and the amplitudes of HFCC airflow signals again become greater than spontaneous airflow signals. To emphasize the difference of HFCC airflow signals, phase II were consciously prolonged and they are longer than phase II in fig. 6.
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Related In: Results  -  Collection

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Figure 5: The airflow signals measured at the mouth of the subject, the high-pass filtered (HFCC airflow signal) and the low-pass filtered (spontaneous airflow signal) curves. The subject was using HFCC with (a) 5 Hz, (b) 15 Hz, and (c) 21 Hz. Regardless of the frequencies, larger spontaneous airflow signals result in smaller HFCC airflow signals. Since it is difficult to breathe hard during 21 Hz, spontaneous airflow signals in (c) are smaller than in (a) and (b). Phase I is the portion of the inspiration phase when spontaneous airflow signals are greater than the amplitudes of HFCC airflow signals. During phase II, spontaneous airflow signals stay within the amplitudes of HFCC airflow signals. In phase III, expiration begins, and the amplitudes of airflow oscillation decrease to about the amplitudes during phase I. Spontaneous airflow signals get greater to the negative direction than HFCC airflow signals. Finally phase IV, the resting period before inspiration, begins, and the amplitudes of HFCC airflow signals again become greater than spontaneous airflow signals. To emphasize the difference of HFCC airflow signals, phase II were consciously prolonged and they are longer than phase II in fig. 6.
Mentions: Fig. 4 is the airflow the oesophageal pressure measured by Fink et al. [6], which shows typical respiratory airflow during use of HFCC. Fig. 5 shows the subject's airflow signals during ten seconds at the three different frequencies. In this figure, each airflow signal is also viewed as the low-pass filtered and the high-pass filtered curves. The high-pass filtered curve indicates the fast airflow signal oscillation generated by HFCC pulses (HFCC airflow signal) whereas the low-pass filtered curve is the airflow during the spontaneous breathing effort (spontaneous airflow signal) of the subject. We divided one cycle of the respiration into four phases. Phase I is the portion of the inspiration phase when the spontaneous airflow signal is greater than the amplitudes of HFCC airflow signal. In this phase LV increases. During phase II, the spontaneous airflow signal stays within the amplitudes of the HFCC airflow signal. Phase II is the pause before expiration begins. The amplitudes of HFCC airflow signals change considerably as the phase moves from I to II. To emphasize the difference of HFCC airflow signals, phase II were consciously prolonged and they are longer than phase II in fig. 6. In phase III, passive or active expiration begins and the amplitudes of HFCC airflow signals decrease to about phase I amplitudes. The low-pass curve gets greater to the negative direction curve than the high-pass curve. The next and last phase is phase IV. Phase IV is the resting period before inspiration, begins. During this phase the amplitudes of HFCC airflow signals again become greater than the spontaneous airflow signal. To reproduce similar airflow signals, a cycle of respiration from phase IV to phase IV was simulated using our computational model. The simulation results with the three frequencies are shown in fig. 6.

Bottom Line: The simulation results indicated that lung capacitance or the inertance of air is also not a factor in the non-uniformity of HFCC airflow signals.Although not perfect, our circuit analogue model allows us to effectively simulate the nonlinear characteristics of the respiratory system.We found that the amplitudes of HFCC airflow signals behave as a function of spontaneous airflow signals.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA. sohn0015@umn.edu

ABSTRACT

Background: High frequency chest compression (HFCC) is a useful and popular therapy for clearing bronchial airways of excessive or thicker mucus. Our observation of respiratory airflow of a subject during use of HFCC showed the airflow oscillation by HFCC was strongly influenced by the nonlinearity of the respiratory system. We used a computational model-based approach to analyse the respiratory airflow during use of HFCC.

Methods: The computational model, which is based on previous physiological studies and represented by an electrical circuit analogue, was used for simulation of in vivo protocol that shows the nonlinearity of the respiratory system. Besides, airflow was measured during use of HFCC. We compared the simulation results to either the measured data or the previous research, to understand and explain the observations.

Results and discussion: We could observe two important phenomena during respiration pertaining to the airflow signal oscillation generated by HFCC. The amplitudes of HFCC airflow signals varied depending on spontaneous airflow signals. We used the simulation results to investigate how the nonlinearity of airway resistance, lung capacitance, and inertance of air characterized the respiratory airflow. The simulation results indicated that lung capacitance or the inertance of air is also not a factor in the non-uniformity of HFCC airflow signals. Although not perfect, our circuit analogue model allows us to effectively simulate the nonlinear characteristics of the respiratory system.

Conclusion: We found that the amplitudes of HFCC airflow signals behave as a function of spontaneous airflow signals. This is due to the nonlinearity of the respiratory system, particularly variations in airway resistance.

Show MeSH