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dc.contributor.authorSaatçı, Esra
dc.date.accessioned2018-07-27T07:31:54Z
dc.date.available2018-07-27T07:31:54Z
dc.date.issued2018-01
dc.identifier.issn0169-2607
dc.identifier.other1872-7565
dc.identifier.urihttps://doi.org/10.1016/j.cmpb.2017.10.003
dc.identifier.urihttps://hdl.handle.net/11413/2379
dc.description.abstractBackground and objectives: The understanding of the bonds and the relationships between the respiratory signals, i.e. the airflow, the mouth pressure, the relative temperature and the relative humidity during breathing may provide the improvement on the measurement methods of respiratory mechanics and sensor designs or the exploration of the several possible applications in the analysis of respiratory disorders. Therefore, the main objective of this study was to propose a new combination of methods in order to determine the relationship between respiratory signals as a multidimensional data. Methods: In order to reveal the coupling between the processes two very different methods were used: the well-known statistical correlation analysis (i.e. Pearson's correlation and cross-correlation coefficient) and parallel coordinate plots (PCPs). Curve bundling with the number intersections for the correlation analysis, Least Mean Square Time Delay Estimator (LMS-TDE) for the point delay detection and visual metrics for the recognition of the visual structures were proposed and utilized in PCP. Results: The number of intersections was increased when the correlation coefficient changed from high positive to high negative correlation between the respiratory signals, especially if whole breath was processed. LMS-TDE coefficients plotted in PCP indicated well-matched point delay results to the findings in the correlation analysis. Visual inspection of PCB by visual metrics showed range, dispersions, entropy comparisons and linear and sinusoidal-like relationships between the respiratory signals. Conclusion: It is demonstrated that the basic correlation analysis together with the parallel coordinate plots perceptually motivates the visual metrics in the display and thus can be considered as an aid to the user analysis by providing meaningful views of the data. (C) 2017 Elsevier B.V. All rights reserved. Methods: In order to reveal the coupling between the processes two very different methods were used: the well-known statistical correlation analysis (i.e. Pearson's correlation and cross-correlation coefficient) and parallel coordinate plots (PCPs). Curve bundling with the number intersections for the correlation analysis, Least Mean Square Time Delay Estimator (LMS-TDE) for the point delay detection and visual metrics for the recognition of the visual structures were proposed and utilized in PCP. Results: The number of intersections was increased when the correlation coefficient changed from high positive to high negative correlation between the respiratory signals, especially if whole breath was processed. LMS-TDE coefficients plotted in PCP indicated well-matched point delay results to the findings in the correlation analysis. Visual inspection of PCB by visual metrics showed range, dispersions, entropy comparisons and linear and sinusoidal-like relationships between the respiratory signals. Conclusion: It is demonstrated that the basic correlation analysis together with the parallel coordinate plots perceptually motivates the visual metrics in the display and thus can be considered as an aid to the user analysis by providing meaningful views of the data. (C) 2017 Elsevier B.V. All rights reserved.tr_TR
dc.language.isoen_UStr_TR
dc.publisherElsevier Ireland Ltd, Elsevier House, Brookvale Plaza, East Park Shannon, Co, Clare, 00000, Irelandtr_TR
dc.relationComputer Methods and Programs in Biomedicinetr_TR
dc.subjectStatistical analysis of biomedical signalstr_TR
dc.subjectRespiratory signal modelingtr_TR
dc.subjectParallel coordinate plots of the respiratory signalstr_TR
dc.subjectAir-Flowtr_TR
dc.titleCorrelation analysis of respiratory signals by using parallel coordinate plotstr_TR
dc.typeArticletr_TR
dc.contributor.authorID112197tr_TR
dc.identifier.wos416507300006
dc.identifier.wos416507300006en
dc.identifier.scopus2-s2.0-85030785335
dc.identifier.scopus2-s2.0-85030785335en
dc.identifier.pubmed29157460
dc.identifier.pubmed29157460en


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