Browsing by Author "Skoric, Tamara (57038835800)"

Objectives Personalised monitoring in health applications has been recognised as part of the mobile crowdsensing concept, where subjects equipped with sensors extract information and share them for personal or common benefit. Limited transmission resources impose the use of local analyses methodology, but this approach is incompatible with analytical tools that require stationary and artefact-free data. This paper proposes a computationally efficient binarised cross-approximate entropy, referred to as (X)BinEn, for unsupervised cardiovascular signal processing in environments where energy and processor resources are limited. Methods The proposed method is a descendant of the cross-approximate entropy ((X)ApEn). It operates on binary, differentially encoded data series split into m-sized vectors. The Hamming distance is used as a distance measure, while a search for similarities is performed on the vector sets. The procedure is tested on rats under shaker and restraint stress, and compared to the existing (X)ApEn results. Results The number of processing operations is reduced. (X)BinEn captures entropy changes in a similar manner to (X)ApEn. The coding coarseness yields an adverse effect of reduced sensitivity, but it attenuates parameter inconsistency and binary bias. A special case of (X)BinEn is equivalent to Shannon's entropy. A binary conditional entropy for m =1 vectors is embedded into the (X)BinEn procedure. Conclusion (X)BinEn can be applied to a single time series as an auto-entropy method, or to a pair of time series, as a cross-entropy method. Its low processing requirements makes it suitable for mobile, battery operated, self-attached sensing devices, with limited power and processor resources. © 2016

Objectives Personalised monitoring in health applications has been recognised as part of the mobile crowdsensing concept, where subjects equipped with sensors extract information and share them for personal or common benefit. Limited transmission resources impose the use of local analyses methodology, but this approach is incompatible with analytical tools that require stationary and artefact-free data. This paper proposes a computationally efficient binarised cross-approximate entropy, referred to as (X)BinEn, for unsupervised cardiovascular signal processing in environments where energy and processor resources are limited. Methods The proposed method is a descendant of the cross-approximate entropy ((X)ApEn). It operates on binary, differentially encoded data series split into m-sized vectors. The Hamming distance is used as a distance measure, while a search for similarities is performed on the vector sets. The procedure is tested on rats under shaker and restraint stress, and compared to the existing (X)ApEn results. Results The number of processing operations is reduced. (X)BinEn captures entropy changes in a similar manner to (X)ApEn. The coding coarseness yields an adverse effect of reduced sensitivity, but it attenuates parameter inconsistency and binary bias. A special case of (X)BinEn is equivalent to Shannon's entropy. A binary conditional entropy for m =1 vectors is embedded into the (X)BinEn procedure. Conclusion (X)BinEn can be applied to a single time series as an auto-entropy method, or to a pair of time series, as a cross-entropy method. Its low processing requirements makes it suitable for mobile, battery operated, self-attached sensing devices, with limited power and processor resources. © 2016

In this paper a copula approach is applied as a tool for assessing the measure of statistical dependence of parallel cardiovascular time series. Families of Archimedean copulas (Clayton, Frank and Gumbel) are applied to pulse interval, systolic and diastolic blood pressure recorded from male Wistar rats at baseline conditions, and to their isodistributional surrogates with the same marginal, but randomized joint distribution functions. The influence of time offset of the parallel time series is explored. The amount of data required for a stable working point is discussed. © 2015 IEEE.

In this paper a copula approach is applied as a tool for assessing the measure of statistical dependence of parallel cardiovascular time series. Families of Archimedean copulas (Clayton, Frank and Gumbel) are applied to pulse interval, systolic and diastolic blood pressure recorded from male Wistar rats at baseline conditions, and to their isodistributional surrogates with the same marginal, but randomized joint distribution functions. The influence of time offset of the parallel time series is explored. The amount of data required for a stable working point is discussed. © 2015 IEEE.

Copula is a (cumulative) distribution function with a density that visualizes the dependency structure of two or more time series. Frank's copula is well suited for systolic blood pressure (SBP) and pulse interval (PI) signal pairs, but the copula analyses are restricted to the probabilistic domain. A new, single-dimensional, time series that reflects the fluctuations of the signal dependency level can be obtained by mapping the signal coupling strength at a beat-by-beat basis. Such a transformation requires a probability density estimation. This paper compares several methods of density estimation to produce a time series that correspond to the dependency fluctuation of SBP and PI time series. As an illustrative example, vasopressin selective V1a and V2 receptor antagonists were employed to modulate simultaneously multiple physiological functions. © 2020 IEEE.

Copula is a (cumulative) distribution function with a density that visualizes the dependency structure of two or more time series. Frank's copula is well suited for systolic blood pressure (SBP) and pulse interval (PI) signal pairs, but the copula analyses are restricted to the probabilistic domain. A new, single-dimensional, time series that reflects the fluctuations of the signal dependency level can be obtained by mapping the signal coupling strength at a beat-by-beat basis. Such a transformation requires a probability density estimation. This paper compares several methods of density estimation to produce a time series that correspond to the dependency fluctuation of SBP and PI time series. As an illustrative example, vasopressin selective V1a and V2 receptor antagonists were employed to modulate simultaneously multiple physiological functions. © 2020 IEEE.

Objectives: Copula is a tool for measuring linear and non-linear interactions between two or more time series. The aim of this paper is to prove that a copula approach can accurately capture and visualize the spatial and temporal fluctuations in dependency structures of cardiovascular signals, and to outline the application possibilities. Methods: The method for measuring the level of interaction between systolic blood pressure and the corresponding pulse interval is validated statistically and pharmacologically. The time series are recorded from the freely moving male Wistar rats equipped with radio-telemetry device for blood pressure recording, before and after administration of autonomic blockers scopolamine, atenolol, prazosin and hexamethonium. Implicit (Gaussian and t) and explicit (Clayton, Frank and Gumbel) copulas were calculated and compared to the conventional bivariate methods (Kendal, Pearson, Spearman and classical correlation). Further statistical validation was done using artificially generated surrogate data. A window sliding procedure for dynamic monitoring the signals’ coupling strength is implemented. Results: Under the baseline physiological conditions, SBP-PI dependency is significant for time lags 0 s–4 s. Hexamethonium completely abolished the dependency, scopolamine abolished it for time lags 0 s–2 s, atenolol first slightly increased, than for lags greater than 2 s decreased the dependency and prazosin had no effect. Isospectral and isodistributional surrogate data tests confirm that copulas successfully notify the absence of dependency as well. Conclusion: Copula approach accurately captures the temporal fluctuations in dependency structures of SBP and PI, simultaneously enabling a visualization of dependency levels within the particular signal zones. An analysis showed that copulas are more sensitive than the conventional statistical measures, with Frank copula exhibiting the best characterization of SBP and PI dependency. © 2018 The Authors

Objectives: Copula is a tool for measuring linear and non-linear interactions between two or more time series. The aim of this paper is to prove that a copula approach can accurately capture and visualize the spatial and temporal fluctuations in dependency structures of cardiovascular signals, and to outline the application possibilities. Methods: The method for measuring the level of interaction between systolic blood pressure and the corresponding pulse interval is validated statistically and pharmacologically. The time series are recorded from the freely moving male Wistar rats equipped with radio-telemetry device for blood pressure recording, before and after administration of autonomic blockers scopolamine, atenolol, prazosin and hexamethonium. Implicit (Gaussian and t) and explicit (Clayton, Frank and Gumbel) copulas were calculated and compared to the conventional bivariate methods (Kendal, Pearson, Spearman and classical correlation). Further statistical validation was done using artificially generated surrogate data. A window sliding procedure for dynamic monitoring the signals’ coupling strength is implemented. Results: Under the baseline physiological conditions, SBP-PI dependency is significant for time lags 0 s–4 s. Hexamethonium completely abolished the dependency, scopolamine abolished it for time lags 0 s–2 s, atenolol first slightly increased, than for lags greater than 2 s decreased the dependency and prazosin had no effect. Isospectral and isodistributional surrogate data tests confirm that copulas successfully notify the absence of dependency as well. Conclusion: Copula approach accurately captures the temporal fluctuations in dependency structures of SBP and PI, simultaneously enabling a visualization of dependency levels within the particular signal zones. An analysis showed that copulas are more sensitive than the conventional statistical measures, with Frank copula exhibiting the best characterization of SBP and PI dependency. © 2018 The Authors