Authors: A Garde B F Giraldo R Jané T D Latshang A J Turk T Hess M M Bosch D Barthelmes T M Merz J Pichler Hefti O D Schoch K E Bloch
Publish Date: 2015/03/31
Volume: 53, Issue: 8, Pages: 699-712
Abstract
This work investigates the performance of cardiorespiratory analysis detecting periodic breathing PB in chest wall recordings in mountaineers climbing to extreme altitude The breathing patterns of 34 mountaineers were monitored unobtrusively by inductance plethysmography ECG and pulse oximetry using a portable recorder during climbs at altitudes between 4497 and 7546 m on Mt Muztagh Ata The minute ventilation VE and heart rate HR signals were studied to identify visually scored PB applying timevarying spectral coherence and entropy analysis In 411 climbing periods 30–120 min in duration high values of mean power MPVE and slope MSlopeVE of the modulation frequency band of VE accurately identified PB with an area under the ROC curve of 88 and 89 respectively Prolonged stay at altitude was associated with an increase in PB During PB episodes higher peak power of ventilatory MPVE and cardiac MP LF HR oscillations and cardiorespiratory coherence MP LF Coher but reduced ventilation entropy SampEnVE was observed Therefore the characterization of cardiorespiratory dynamics by the analysis of VE and HR signals accurately identifies PB and effects of altitude acclimatization providing promising tools for investigating physiologic effects of environmental exposures and diseasesThis work was supported by an international cooperation Grant of the Swiss National Science Foundation SNSF a mobility grant of the CIBER de Bioingeniería Biomateriales y Nanomedicina CIBERBBN and by the Ministerio de Economía y Competitividad from Spanish Government under Grant TEC201021703C0301 and by Grants from the Lung Ligue of Zurich SwitzerlandThe advantage of modelbased frequency estimation is its capacity to predict future samples outside of the observation interval instead of assuming zero as occurs with conventional nonparametric or Fourierbased spectral analysis 34 The accuracy of the AR model was evaluated through the mean square prediction error The optimum model order ranging from 2 to 50 was selected for each signal according to the criterion proposed by Rissanen 30 based on selecting the model order that minimizes the description lengthApproximate entropy ApEn and sample entropy SampEn provide quantitative information about the complexity of the signals ApEn is approximately equal to the negative average natural logarithm of the conditional probability that two sequences that are similar for m points remain similar that is within a tolerance r at the next point In order to avoid the occurrence of ln0 in the calculations ApEn algorithm counts each sequence as matching itself ApEn is therefore heavily dependent on the record length and lacks relative consistency SampEn is the negative natural logarithm of the conditional probability that two sequences similar for m points remain similar at the next point where selfmatches are not included in calculating the probability 8
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