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. 2016 Oct 25;16(11):1779.
doi: 10.3390/s16111779.

Elimination of Drifts in Long-Duration Monitoring for Apnea-Hypopnea of Human Respiration

Peng Jiang  1 , Rong Zhu  2
Affiliations

Elimination of Drifts in Long-Duration Monitoring for Apnea-Hypopnea of Human Respiration

Peng Jiang et al. Sensors (Basel). .

Abstract

This paper reports a methodology to eliminate an uncertain baseline drift in respiratory monitoring using a thermal airflow sensor exposed in a high humidity environment. Human respiratory airflow usually contains a large amount of moisture (relative humidity, RH > 85%). Water vapors in breathing air condense gradually on the surface of the sensor so as to form a thin water film that leads to a significant sensor drift in long-duration respiratory monitoring. The water film is formed by a combination of condensation and evaporation, and therefore the behavior of the humidity drift is complicated. Fortunately, the exhale and inhale responses of the sensor exhibit distinguishing features that are different from the humidity drift. Using a wavelet analysis method, we removed the baseline drift of the sensor and successfully recovered the respiratory waveform. Finally, we extracted apnea-hypopnea events from the respiratory signals monitored in whole-night sleeps of patients and compared them with golden standard polysomnography (PSG) results.

Keywords: hot-film airflow sensor; humidity drift; sleep apnea; wavelet analysis.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
(a) Hot-film installation in a tube for respiration monitoring; (b) Hot-film installation on surface of sensing strip adhered underneath nostril.
Figure 2
Figure 2
(a) Configuration of hot-film sensor; (b) Sensor prototype; (c) CTD circuit.
Figure 3
Figure 3
(a) Schematic view of hot-film covered by thin water film in 98% RH wet air; (b) Simulated temperature distribution around the hot-film; (c) Hot-film temperature changing with the thickness of the water film.
Figure 4
Figure 4
Sensor drift under different humidity environments.
Figure 5
Figure 5
Measurements on alternating airflow mimicking respiratory fluctuation in moist air.
Figure 6
Figure 6
Sym8 scaling function and wavelet function with their amplitude frequency response.
Figure 7
Figure 7
Discrete wavelet transform decomposition processing diagram.
Figure 8
Figure 8
DWT-based reconstructed signal for drift elimination.
Figure 9
Figure 9
Identification of apnea-hypopnea events from respiratory signals by the wavelet algorithm.
Figure 10
Figure 10
Feature extraction algorithm for apnea and hypopnea events.
See this image and copyright information in PMC

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