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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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Short segments of example signals in sitting (upper) and supine (lower) body positions. Spirometer signals (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 3: Short segments of example signals in sitting (upper) and supine (lower) body positions. Spirometer signals (black) and the predicted airflows (red: the standard method, blue: proposed method with N=8, green: proposed method with N=16).

Mentions: Figure 3 depicts a short segment of example signals from both body positions. The predicted airflow with the N values 8 and 16 followed much more accurately the spirometer signal than that with the standard method. Predicted airflows with the N values of 8 and 16 are almost completely overlapping visually.


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)

Short segments of example signals in sitting (upper) and supine (lower) body positions. Spirometer signals (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 3: Short segments of example signals in sitting (upper) and supine (lower) body positions. Spirometer signals (black) and the predicted airflows (red: the standard method, blue: proposed method with N=8, green: proposed method with N=16).
Mentions: Figure 3 depicts a short segment of example signals from both body positions. The predicted airflow with the N values 8 and 16 followed much more accurately the spirometer signal than that with the standard method. Predicted airflows with the N values of 8 and 16 are almost completely overlapping visually.

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