Efficient Computation of Multiscale Entropy over Short Biomedical Time Series Based on Linear State-Space Models
The most common approach to assess the dynamical complexity of a time series across multiple temporal scales makes use of the multiscale entropy (MSE) and refined MSE (RMSE) measures. In spite of their popularity, MSE and RMSE lack an analytical framework allowing their calculation for known dynamic...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2017/1768264 |
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| author | Luca Faes Alberto Porta Michal Javorka Giandomenico Nollo |
| author_facet | Luca Faes Alberto Porta Michal Javorka Giandomenico Nollo |
| author_sort | Luca Faes |
| collection | DOAJ |
| description | The most common approach to assess the dynamical complexity of a time series across multiple temporal scales makes use of the multiscale entropy (MSE) and refined MSE (RMSE) measures. In spite of their popularity, MSE and RMSE lack an analytical framework allowing their calculation for known dynamic processes and cannot be reliably computed over short time series. To overcome these limitations, we propose a method to assess RMSE for autoregressive (AR) stochastic processes. The method makes use of linear state-space (SS) models to provide the multiscale parametric representation of an AR process observed at different time scales and exploits the SS parameters to quantify analytically the complexity of the process. The resulting linear MSE (LMSE) measure is first tested in simulations, both theoretically to relate the multiscale complexity of AR processes to their dynamical properties and over short process realizations to assess its computational reliability in comparison with RMSE. Then, it is applied to the time series of heart period, arterial pressure, and respiration measured for healthy subjects monitored in resting conditions and during physiological stress. This application to short-term cardiovascular variability documents that LMSE can describe better than RMSE the activity of physiological mechanisms producing biological oscillations at different temporal scales. |
| format | Article |
| id | doaj-art-03147b2665d244779845b907f2fed0cd |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-03147b2665d244779845b907f2fed0cd2025-02-03T01:12:24ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/17682641768264Efficient Computation of Multiscale Entropy over Short Biomedical Time Series Based on Linear State-Space ModelsLuca Faes0Alberto Porta1Michal Javorka2Giandomenico Nollo3BIOtech, Department of Industrial Engineering, University of Trento, Trento, ItalyDepartment of Biomedical Sciences for Health, University of Milan, Milan, ItalyDepartment of Physiology, Jessenius Faculty of Medicine, Comenius University in Bratislava, Mala Hora 4C, 03601 Martin, SlovakiaBIOtech, Department of Industrial Engineering, University of Trento, Trento, ItalyThe most common approach to assess the dynamical complexity of a time series across multiple temporal scales makes use of the multiscale entropy (MSE) and refined MSE (RMSE) measures. In spite of their popularity, MSE and RMSE lack an analytical framework allowing their calculation for known dynamic processes and cannot be reliably computed over short time series. To overcome these limitations, we propose a method to assess RMSE for autoregressive (AR) stochastic processes. The method makes use of linear state-space (SS) models to provide the multiscale parametric representation of an AR process observed at different time scales and exploits the SS parameters to quantify analytically the complexity of the process. The resulting linear MSE (LMSE) measure is first tested in simulations, both theoretically to relate the multiscale complexity of AR processes to their dynamical properties and over short process realizations to assess its computational reliability in comparison with RMSE. Then, it is applied to the time series of heart period, arterial pressure, and respiration measured for healthy subjects monitored in resting conditions and during physiological stress. This application to short-term cardiovascular variability documents that LMSE can describe better than RMSE the activity of physiological mechanisms producing biological oscillations at different temporal scales.http://dx.doi.org/10.1155/2017/1768264 |
| spellingShingle | Luca Faes Alberto Porta Michal Javorka Giandomenico Nollo Efficient Computation of Multiscale Entropy over Short Biomedical Time Series Based on Linear State-Space Models Complexity |
| title | Efficient Computation of Multiscale Entropy over Short Biomedical Time Series Based on Linear State-Space Models |
| title_full | Efficient Computation of Multiscale Entropy over Short Biomedical Time Series Based on Linear State-Space Models |
| title_fullStr | Efficient Computation of Multiscale Entropy over Short Biomedical Time Series Based on Linear State-Space Models |
| title_full_unstemmed | Efficient Computation of Multiscale Entropy over Short Biomedical Time Series Based on Linear State-Space Models |
| title_short | Efficient Computation of Multiscale Entropy over Short Biomedical Time Series Based on Linear State-Space Models |
| title_sort | efficient computation of multiscale entropy over short biomedical time series based on linear state space models |
| url | http://dx.doi.org/10.1155/2017/1768264 |
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