A Fast DFA Algorithm for Multifractal Multiscale Analysis of Physiological Time Series

Background: The widespread adoption of electronic health records allows us to ask evidence-based questions about the need for and benefits of specific clinical interventions in critical-care settings across large populations. Objective: We investigated the prediction of vasopressor administration and weaning in the intensive care unit. Vasopressors are commonly used to control hypotension, and changes in timing and dosage can have a large impact on patient outcomes. Materials and Methods: We considered a cohort of 15 695 intensive care unit patients without orders for reduced care who were alive 30 days post-discharge. A switching-state autoregressive model (SSAM) was trained to predict the multidimensional physiological time series of patients before, during, and after vasopressor administration. The latent states from the SSAM were used as predictors of vasopressor administration and weaning. Results: The unsupervised SSAM features were able to predict patient vasopressor administration and successful patient weaning. Features derived from the SSAM achieved areas under the receiver operating curve of 0.92, 0.88, and 0.71 for predicting ungapped vasopressor administration, gapped vasopressor administration, and vasopressor weaning, respectively. We also demonstrated many cases where our model predicted weaning well in advance of a successful wean. Conclusion: Models that used SSAM features increased performance on both predictive tasks. These improvements may reflect an underlying, and ultimately predictive, latent state detectable from the physiological time series.

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Diagnosis and Monitoring of Volatile Fatty Acids Production from Raw Cheese Whey by Multiscale Time-Series Analysis

Applied Sciences ◽

10.3390/app11135803 ◽

2021 ◽

Vol 11 (13) ◽

pp. 5803

Author(s):

Antonio Lara-Musule ◽

Ervin Alvarez-Sanchez ◽

Gloria Trejo-Aguilar ◽

Laura Acosta-Dominguez ◽

Hector Puebla ◽

...

Keyword(s):

Fatty Acids ◽

Time Series ◽

Time Series Analysis ◽

Multiscale Analysis ◽

Volatile Fatty Acids ◽

Cheese Whey ◽

Anaerobic Treatment ◽

Operating Conditions ◽

Series Analysis ◽

Key Variables

Anaerobic treatment is a viable alternative for the treatment of agro-industrial waste. Anaerobic digestion reduces organic load and produces volatile fatty acids (VFA), which are precursors of value-added products such as methane-rich biogas, biohydrogen, and biopolymers. Nowadays, there are no low-cost diagnosis and monitoring systems that analyze the dynamic behavior of key variables in real time, representing a significant limitation for its practical implementation. In this work, the feasibility of using the multiscale analysis to diagnose and monitor the key variables in VFA production by anaerobic treatment of raw cheese whey is presented. First, experiments were carried out to evaluate the performance of the proposed methodology under different operating conditions. Then, experimental pH time series were analyzed using rescaled range (R/S) techniques. Time-series analysis shows that the anaerobic VFA production exhibits a multiscale behavior, identifying three characteristic regions (i.e., three values of Hurst exponent). In addition, the dynamic Hurst exponents show satisfactory correlations with the chemical oxygen demand (COD) consumption and VFA production. The multiscale analysis of pH time series is easy to implement and inexpensive. Hence, it could be used as a diagnosis and indirect monitoring system of key variables in the anaerobic treatment of raw cheese whey.

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Some Remarks on Nonlinear Data Analysis of Physiological Time Series

NATO ASI Series - Measures of Complexity and Chaos ◽

10.1007/978-1-4757-0623-9_4 ◽

1989 ◽

pp. 51-62 ◽

Cited By ~ 12

Author(s):

A. Babloyantz

Keyword(s):

Time Series ◽

Data Analysis ◽

Physiological Time ◽

Nonlinear Data Analysis

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Quantifying Memory in Complex Physiological Time-Series

PLoS ONE ◽

10.1371/journal.pone.0072854 ◽

2013 ◽

Vol 8 (9) ◽

pp. e72854 ◽

Cited By ~ 19

Author(s):

Amir H. Shirazi ◽

Mohammad R. Raoufy ◽

Haleh Ebadi ◽

Michele De Rui ◽

Sami Schiff ◽

...

Keyword(s):

Time Series ◽

Physiological Time

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Nonlinear Analysis of Physiological Time Series

Advanced Biosignal Processing ◽

10.1007/978-3-540-89506-0_15 ◽

2009 ◽

pp. 307-333 ◽

Cited By ~ 10

Author(s):

Anisoara Paraschiv-Ionescu ◽

Kamiar Aminian

Keyword(s):

Time Series ◽

Nonlinear Analysis ◽

Physiological Time

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Ordinal Patterns in Heartbeat Time Series: An Approach Using Multiscale Analysis

Entropy ◽

10.3390/e21060583 ◽

2019 ◽

Vol 21 (6) ◽

pp. 583 ◽

Cited By ~ 2

Author(s):

María Muñoz-Guillermo

Keyword(s):

Time Series ◽

Multiscale Analysis ◽

Renyi Entropy ◽

Rényi Entropy ◽

Cardiac Diseases ◽

Ordinal Patterns

In this paper, we simultaneously use two different scales in the analysis of ordinal patterns to measure the complexity of the dynamics of heartbeat time series. Rényi entropy and weighted Rényi entropy are the entropy-like measures proposed in the multiscale analysis in which, with the new scheme, four parameters are involved. First, the influence of the variation of the new parameters in the entropy values is analyzed when different groups of subjects (with cardiac diseases or healthy) are considered. Secondly, we exploit the introduction of multiscale analysis in order to detect differences between the groups.

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