Correlation analysis of respiratory signals by using parallel coordinate plots. (January 2018)

• Record Type: Journal Article
• Authors: Saatci, Esra
• Is Part Of: Computer methods and programs in biomedicine. Volume 153(2018)
• Page Start: 41
• Page End: 51
• Publication Date: 2018-01
• Subjects: Statistical analysis of biomedical signals -- Respiratory signal modeling -- Parallel coordinate plots of the respiratory signals; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.10.003
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Relationships between the respiratory signals provide improvements in the determination of respiratory mechanics. New combination of methods to determine the correlation is proposed. The statistical correlation analysis and parallel coordinate plots (PCP) are used. PCP demonstrated very powerful visualization tool, which has many appealing properties in terms of both qualitative and quantitative tasks in the correlation analysis. Abstract: Background 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.