Entropy Measures as Descriptors to Identify Apneas in Rheoencephalographic Signals

Abstract

Rheoencephalography (REG) is a simple and inexpensive technique that intends to monitor cerebral blood flow (CBF), but its ability to reflect CBF changes has not been extensively proved. Based on the hypothesis that alterations in CBF during apnea should be reflected in REG signals under the form of increased complexity, several entropy metrics were assessed for REG analysis during apnea and resting periods in 16 healthy subjects: approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy (FuzzyEn), corrected conditional entropy (CCE) and Shannon entropy (SE). To compute these entropy metrics, a set of parameters must be defined a priori, such as, for example, the embedding dimension m, and the tolerance threshold r. A thorough analysis of the effects of parameter selection in the entropy metrics was performed, looking for the values optimizing differences between apnea and baseline signals. All entropy metrics, except SE, provided higher values for apnea periods (p-values < 0.025). FuzzyEn outperformed all other metrics, providing the lowest p-value (p = 0.0001), allowing to conclude that REG signals during apnea have higher complexity than in resting periods. Those findings suggest that REG signals reflect CBF changes provoked by apneas, even though further studies are needed to confirm this hypothesis.

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“…Approximate Entropy (ApEn): ApEn approximates the exact regularity statistic Kolmogorov-Sinai entropy and reflects the predictability of a time series by exploring repetitive patterns in the data. It is applicable to noisy data sets [ 37 ]. It relies on the Heaviside function to define the similarity between 2 patterns.…”

“…Consequently, in addition to the selection of N (length over which to compute entropy), m (subsequence length), and r (tolerance in terms of the number of SDs), FuzzyEn requires a fourth parameter, n , the gradient of the boundary of the exponential function used to assess similarity [ 39 ]. FuzzyEn provides a graded similarity instead of binary similarity between parts of the time series [ 37 ].…”

Non-invasive cuff-less blood pressure estimation using a hybrid deep learning model

“…Blood flow in the brain changes periodically by the cardiac cycle, and the pulsatile change of blood flow causes the variation of BIOZ in the brain: electrical conductivity increases and impedance decreases when blood flows into the brain. Brain BIOZ is a non-invasive technique that enables the monitoring of intracranial blood flow, rendering it valuable for diagnosing cerebral diseases such as intracranial hemorrhages [22] and TBI [23]. In addition, Brain BIOZ also plays a crucial role in ICP monitoring [24].…”

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