Key result
A software tool and Matlab library were developed for the assessment of complexity and variability of long physiological time-series of respiration, demonstrating reliable inter-event time extraction.
The presented Matlab library provides tools for extracting inter-event times and calculating power-law scaling exponents from large physiological time-series datasets.
In this paper, the authors examine software implementation and the initial preprocessing of data and tools during the assessment of the complexity and variability of long physiological time-series. The algorithms presented advance a bigger Matlab library devoted to complex system and data analysis. Commercial software is unavailable for many of these functions and is generally unsuitable for use with multi-gigabyte datasets. Reliable inter-event time extraction from input signal is an important step for the presented considerations. Knowing the distribution of the inter-event time distances, it is possible to calculate exponents due to power-law scaling. From a methodology point of view, simulations and considerations with experimental data supported each stage of the work presented. In this paper, initial calibration of the procedures with accessible data confirmed assessments made during earlier studies, which raise objectivity of measurements planned in the future.
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Jabłoński et al. (2011) studied this question. Software tool for assessment of complexity and variability in physiological signals was evaluated. A software tool and Matlab library were developed for the assessment of complexity and variability of long physiological time-series of respiration, demonstrating reliable inter-event time extraction.
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