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Reducing the airflow waveform distortions from breathing style and body position with improved calibration of respiratory effort belts.

Seppänen TM, Alho OP, Seppänen T - Biomed Eng Online (2013)

Bottom Line: It is based on an optimally trained FIR (Finite Impulse Response) filter bank constructed as a MISO system (Multiple-Input Single-Output) between respiratory effort belt signals and the spirometer in order to reduce waveform errors.Relative waveform error decreased 60-70% when predicting airflow under changing breathing styles.Standard deviation of respiratory volume error decreased even 80%.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science and Engineering, University of Oulu, Oulu, Finland. tiina.seppanen@ee.oulu.fi.

ABSTRACT

Background: Respiratory effort belt measurement is a widely used method to monitor respiration. Signal waveforms of respiratory volume and flow may indicate pathological signs of several diseases and, thus, it would be highly desirable to predict them accurately. Calibrated effort belts are sufficiently accurate for estimating respiratory rate, but the respiratory volume and flow prediction accuracies degrade considerably with changes in the subject's body position and breathing style.

Methods: An improved calibration method of respiratory effort belts is presented in this paper. It is based on an optimally trained FIR (Finite Impulse Response) filter bank constructed as a MISO system (Multiple-Input Single-Output) between respiratory effort belt signals and the spirometer in order to reduce waveform errors. Ten healthy adult volunteers were recruited. Breathing was varied between the following styles: metronome-guided controlled breathing rate of 0.1 Hz, 0.15 Hz, 0.25 Hz and 0.33 Hz, and a free rate that was felt normal by each subject. Body position was varied between supine, sitting and standing. The proposed calibration method was tested against these variations and compared with the state-of-the-art methods from the literature.

Results: Relative waveform error decreased 60-70% when predicting airflow under changing breathing styles. The coefficient of determination R2 varied between 0.88-0.95 and 0.65-0.79 with the proposed and the standard method, respectively. Standard deviation of respiratory volume error decreased even 80%. The proposed method outperformed other methods.

Conclusions: Results show that not only the respiratory volume can be computed more precisely from the predicted airflow, but also the flow waveforms are very accurate with the proposed method. The method is robust to breathing style changes and body position changes improving greatly the accuracy of the calibration of respiratory effort belts over the standard method. The enhanced accuracy of the belt calibration offers interesting opportunities, e.g. in pulmonary and critical care medicine when objective measurements are required.

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Example signals from step 12 with body position changes. Spirometer signal (black) and the predicted airflows (red: the standard method, blue: proposed method with N=8, green: proposed method with N=16).
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Figure 9: Example signals from step 12 with body position changes. Spirometer signal (black) and the predicted airflows (red: the standard method, blue: proposed method with N=8, green: proposed method with N=16).

Mentions: Figure 9 depicts example signals from one subject from step 12. At the beginning when the subject was sitting, all predicted airflows follow the original spirometer signal quite well. All predicted airflows deviate from the spirometer signal at the time when body position changed (20 sec and 40 sec points in Figure 9) and proposed method with the FIR filter sizes 8 and 16 overestimate the flow for a short time period. However, the standard method overestimates the flow in a still longer time window. The predicted airflows produced with the proposed method then continue to follow the spirometer signal much more accurately than with the standard method.


Reducing the airflow waveform distortions from breathing style and body position with improved calibration of respiratory effort belts.

Seppänen TM, Alho OP, Seppänen T - Biomed Eng Online (2013)

Example signals from step 12 with body position changes. Spirometer signal (black) and the predicted airflows (red: the standard method, blue: proposed method with N=8, green: proposed method with N=16).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3852323&req=5

Figure 9: Example signals from step 12 with body position changes. Spirometer signal (black) and the predicted airflows (red: the standard method, blue: proposed method with N=8, green: proposed method with N=16).
Mentions: Figure 9 depicts example signals from one subject from step 12. At the beginning when the subject was sitting, all predicted airflows follow the original spirometer signal quite well. All predicted airflows deviate from the spirometer signal at the time when body position changed (20 sec and 40 sec points in Figure 9) and proposed method with the FIR filter sizes 8 and 16 overestimate the flow for a short time period. However, the standard method overestimates the flow in a still longer time window. The predicted airflows produced with the proposed method then continue to follow the spirometer signal much more accurately than with the standard method.

Bottom Line: It is based on an optimally trained FIR (Finite Impulse Response) filter bank constructed as a MISO system (Multiple-Input Single-Output) between respiratory effort belt signals and the spirometer in order to reduce waveform errors.Relative waveform error decreased 60-70% when predicting airflow under changing breathing styles.Standard deviation of respiratory volume error decreased even 80%.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science and Engineering, University of Oulu, Oulu, Finland. tiina.seppanen@ee.oulu.fi.

ABSTRACT

Background: Respiratory effort belt measurement is a widely used method to monitor respiration. Signal waveforms of respiratory volume and flow may indicate pathological signs of several diseases and, thus, it would be highly desirable to predict them accurately. Calibrated effort belts are sufficiently accurate for estimating respiratory rate, but the respiratory volume and flow prediction accuracies degrade considerably with changes in the subject's body position and breathing style.

Methods: An improved calibration method of respiratory effort belts is presented in this paper. It is based on an optimally trained FIR (Finite Impulse Response) filter bank constructed as a MISO system (Multiple-Input Single-Output) between respiratory effort belt signals and the spirometer in order to reduce waveform errors. Ten healthy adult volunteers were recruited. Breathing was varied between the following styles: metronome-guided controlled breathing rate of 0.1 Hz, 0.15 Hz, 0.25 Hz and 0.33 Hz, and a free rate that was felt normal by each subject. Body position was varied between supine, sitting and standing. The proposed calibration method was tested against these variations and compared with the state-of-the-art methods from the literature.

Results: Relative waveform error decreased 60-70% when predicting airflow under changing breathing styles. The coefficient of determination R2 varied between 0.88-0.95 and 0.65-0.79 with the proposed and the standard method, respectively. Standard deviation of respiratory volume error decreased even 80%. The proposed method outperformed other methods.

Conclusions: Results show that not only the respiratory volume can be computed more precisely from the predicted airflow, but also the flow waveforms are very accurate with the proposed method. The method is robust to breathing style changes and body position changes improving greatly the accuracy of the calibration of respiratory effort belts over the standard method. The enhanced accuracy of the belt calibration offers interesting opportunities, e.g. in pulmonary and critical care medicine when objective measurements are required.

Show MeSH