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Sensors 2016, 16(11), 1779;

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

State Key Laboratory of Precision Measurement Technology and Instrument, Department of Precision Instrument, Tsinghua University, Beijing 100084, China
Author to whom correspondence should be addressed.
Academic Editors: Steffen Leonhardt and Daniel Teichmann
Received: 22 July 2016 / Revised: 28 September 2016 / Accepted: 19 October 2016 / Published: 25 October 2016
(This article belongs to the Special Issue Wearable Biomedical Sensors)
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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. View Full-Text
Keywords: humidity drift; wavelet analysis; sleep apnea; hot-film airflow sensor humidity drift; wavelet analysis; sleep apnea; hot-film airflow sensor

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Jiang, P.; Zhu, R. Elimination of Drifts in Long-Duration Monitoring for Apnea-Hypopnea of Human Respiration. Sensors 2016, 16, 1779.

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