Neural offset time evaluation in surface respiratory signals during controlled respiration
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hdl:2117/183083
Document typeConference lecture
Defense date2019
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessRestricted access - publisher's policy
This work is protected by the corresponding intellectual and industrial property rights.
Except where otherwise noted, its contents are licensed under a Creative Commons license
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Attribution-NonCommercial-NoDerivs 3.0 Spain
Abstract
The electrical activity of the diaphragm measured by surface electromyography (sEMGdi) provides indirect information on neural respiratory drive. Moreover, it allows evaluating the ventilatory pattern from the onset and offset (ntoff) estimation of the neural inspiratory time. sEMGdi amplitude variation was quantified using the fixed sample entropy (fSampEn), a less sensitive method to the interference from cardiac activity. The detection of the ntoff is controversial, since it is located in an intermediate point between the maximum value and the cessation of sEMGdi inspiratory activity, evaluated by the fSampEn. In this work ntoff detection has been analyzed using thresholds between 40% and 100 % of the fSampEn peak. Furthermore, fSampEn was evaluated analyzing the r parameter from 0.05 to 0.6, using a m equal to 1 and a sliding window size equal to 250 ms. The ntoff has been compared to the offset time (toff) obtained from the airflow during a controlled respiratory protocol varying the fractional inspiratory time from 0.54 to 0.18 whilst the respiratory rate was constant at 16 bpm. Results show that the optimal threshold values were between 66.0 % to 77.0 % of the fSampEn peak value. r values between 0.25 to 0.50 were found suitable to be used with the fSampEn.
CitationEstrada, L. [et al.]. Neural offset time evaluation in surface respiratory signals during controlled respiration. A: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. "2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC): proceedings". Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 2344-2347.
ISBN978-1-5386-1311-5
Publisher versionhttps://ieeexplore.ieee.org/document/8856767
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