Why the study?
Does multiscale entropy (MSE) analysis improve the separation of healthy and pathologic physiologic time series compared to traditional algorithms?
Does multiscale entropy (MSE) analysis improve the separation of healthy and pathologic physiologic time series compared to traditional algorithms?
The introduction of multiscale entropy (MSE) provides a more robust computational method to differentiate healthy physiologic dynamics from pathologic processes by accounting for multiple time scales.
MSE may aid physiologic time-series discrimination in simulations; leaves open prospective clinical validation.
There has been considerable interest in quantifying the complexity of physiologic time series, such as heart rate. However, traditional algorithms indicate higher complexity for certain pathologic processes associated with random outputs than for healthy dynamics exhibiting long-range correlations. This paradox may be due to the fact that conventional algorithms fail to account for the multiple time scales inherent in healthy physiologic dynamics. We introduce a method to calculate multiscale entropy (MSE) for complex time series. We find that MSE robustly separates healthy and pathologic groups and consistently yields higher values for simulated long-range correlated noise compared to uncorrelated noise.
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Costa et al. (2002) studied this question.
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