Assessment of Nociceptive Responsiveness Levels during Sedation-Analgesia by Entropy Analysis of EEG

Valencia, J.; Melia, Umberto; Vallverdú, Montserrat; Borrat, X.; Jospin, M.; Jensen, Erik Weber; Porta, Alberto; Gambús, Pedro L.; Caminal, P.

doi:10.3390/e18030103

Cited by 8 publications

(10 citation statements)

References 45 publications

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“…Previous studies have found distinct results for different scales in MSE and suggested that a single scale is not enough for the analysis of biological signals [36,38,40,54,113,127,128]. However, the interpretation of the biological mechanisms reflected by multiple scales has not been well-established.…”

Section: Entropy With Multiple Scales Corresponding To Various Rangesmentioning

confidence: 98%

Altered Brain Complexity in Women with Primary Dysmenorrhea: A Resting-State Magneto-Encephalography Study Using Multiscale Entropy Analysis

Low¹,

Kuo

Liu³

et al. 2017

Entropy

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How chronic pain affects brain functions remains unclear. As a potential indicator, brain complexity estimated by entropy-based methods may be helpful for revealing the underlying neurophysiological mechanism of chronic pain. In this study, complexity features with multiple time scales and spectral features were extracted from resting-state magnetoencephalographic signals of 156 female participants with/without primary dysmenorrhea (PDM) during pain-free state. Revealed by multiscale sample entropy (MSE), PDM patients (PDMs) exhibited loss of brain complexity in regions associated with sensory, affective, and evaluative components of pain, including sensorimotor, limbic, and salience networks. Significant correlations between MSE values and psychological states (depression and anxiety) were found in PDMs, which may indicate specific nonlinear disturbances in limbic and default mode network circuits after long-term menstrual pain. These findings suggest that MSE is an important measure of brain complexity and is potentially applicable to future diagnosis of chronic pain.

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Section: Entropy With Multiple Scales Corresponding To Various Rangesmentioning

confidence: 98%

Altered Brain Complexity in Women with Primary Dysmenorrhea: A Resting-State Magneto-Encephalography Study Using Multiscale Entropy Analysis

Low¹,

Kuo

Liu³

et al. 2017

Entropy

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“…However, these measurements can be extended to provide a multiscale assessment of irregularity of the time series. Refined multiscale entropy (RMSE), proposed in [ 11 ], is a technique that uses SampEn as an entropy-based measure in order to quantify the complexity of a time series in different time scales, which has been applied in processing of electrocardiogram (ECG) and electroencephalogram (EEG) signals [ 12 , 13 , 14 , 15 ]. Computation of RMSE is similar to multiscale entropy (MSE) [ 16 ] except for two significant modifications: (i) RMSE improves the procedure applied to remove the fast time scales in the signal, avoiding the aliasing, and; (ii) it modifies the coarse-graining procedure to avoid an artificial decrease of the entropy as the fast time scales in the signal are eliminated, which is caused by the reduction of the standard deviation that is generated by the filtering process.…”

Section: Introductionmentioning

confidence: 99%

“…This can be very useful, for example, to monitor patients, using physiological signals as the EEG in critical settings such as critical care units. For example, under sedation/anesthesia during surgery the assessment of complexity of the EEG via RMSE was found useful for monitoring the level of consciousness and preventing pain [ 15 ]. The assessment of EEG complexity as a function of time scales using of RMSE is motivated by the observation that the EEG contains oscillations at particular frequency bands, and these oscillations become slower and more regular at higher doses of intravenous anesthetic such as the propofol.…”

Section: Introductionmentioning

confidence: 99%

Refined Multiscale Entropy Using Fuzzy Metrics: Validation and Application to Nociception Assessment

Valencia

Bolaños

Vallverdú

et al. 2019

Entropy

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The refined multiscale entropy (RMSE) approach is commonly applied to assess complexity as a function of the time scale. RMSE is normally based on the computation of sample entropy (SampEn) estimating complexity as conditional entropy. However, SampEn is dependent on the length and standard deviation of the data. Recently, fuzzy entropy (FuzEn) has been proposed, including several refinements, as an alternative to counteract these limitations. In this work, FuzEn, translated FuzEn (TFuzEn), translated-reflected FuzEn (TRFuzEn), inherent FuzEn (IFuzEn), and inherent translated FuzEn (ITFuzEn) were exploited as entropy-based measures in the computation of RMSE and their performance was compared to that of SampEn. FuzEn metrics were applied to synthetic time series of different lengths to evaluate the consistency of the different approaches. In addition, electroencephalograms of patients under sedation-analgesia procedure were analyzed based on the patient’s response after the application of painful stimulation, such as nail bed compression or endoscopy tube insertion. Significant differences in FuzEn metrics were observed over simulations and real data as a function of the data length and the pain responses. Findings indicated that FuzEn, when exploited in RMSE applications, showed similar behavior to SampEn in long series, but its consistency was better than that of SampEn in short series both over simulations and real data. Conversely, its variants should be utilized with more caution, especially whether processes exhibit an important deterministic component and/or in nociception prediction at long scales.

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“…It is noteworthy that multiscale entropy (MSE) analysis (Costa et al, 2002 , 2005 ; Yang and Tsai, 2013 ; Courtiol et al, 2016 ) calculates a series of sample entropy over multiple time scales, which captures the temporal complexity characteristcs of time-series neural signals from microscopic to macroscopic aspects. Recently, MSE analysis has also been applied to brain signals (Heisz and McIntosh, 2013 ; Yang et al, 2013 ; Courtiol et al, 2016 ), pain (Sitges et al, 2010 ; Valencia et al, 2016 ; Liu Q. et al, 2017 ), and PDM studies (Kuo et al, 2017 ; Low et al, 2017 ). By applying MSE analysis on resting-state magnetoencephalography (MEG) signals acquired from PDMs during pain-free state, we observed a general loss of regional complexity in PDMs at brain regions related to chronic pain, including the limbic circuitry, default mode network, sensorimotor network, and salience network (Low et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Interactions of BDNF Val66Met Polymorphism and Menstrual Pain on Brain Complexity

Low

Kuo

Tsai³

et al. 2018

Front. Neurosci.

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The irregularity and uncertainty of neurophysiologic signals across different time scales can be regarded as neural complexity, which is related to the adaptability of the nervous system and the information processing between neurons. We recently reported general loss of brain complexity, as measured by multiscale sample entropy (MSE), at pain-related regions in females with primary dysmenorrhea (PDM). However, it is unclear whether this loss of brain complexity is associated with inter-subject genetic variations. Brain-derived neurotrophic factor (BDNF) is a widely expressed neurotrophin in the brain and is crucial to neural plasticity. The BDNF Val66Met single-nucleotide polymorphism (SNP) is associated with mood, stress, and pain conditions. Therefore, we aimed to examine the interactions of BDNF Val66Met polymorphism and long-term menstrual pain experience on brain complexity. We genotyped BDNF Val66Met SNP in 80 PDM females (20 Val/Val, 31 Val/Met, 29 Met/Met) and 76 healthy female controls (25 Val/Val, 36 Val/Met, 15 Met/Met). MSE analysis was applied to neural source activity estimated from resting-state magnetoencephalography (MEG) signals during pain-free state. We found that brain complexity alterations were associated with the interactions of BDNF Val66Met polymorphism and menstrual pain experience. In healthy female controls, Met carriers (Val/Met and Met/Met) demonstrated lower brain complexity than Val/Val homozygotes in extensive brain regions, suggesting a possible protective role of Val/Val homozygosity in brain complexity. However, after experiencing long-term menstrual pain, the complexity differences between different genotypes in healthy controls were greatly diminished in PDM females, especially in the limbic system, including the hippocampus and amygdala. Our results suggest that pain experience preponderantly affects the effect of BDNF Val66Met polymorphism on brain complexity. The results of the present study also highlight the potential utilization of resting-state brain complexity for the development of new therapeutic strategies in patients with chronic pain.

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Assessment of Nociceptive Responsiveness Levels during Sedation-Analgesia by Entropy Analysis of EEG

Cited by 8 publications

References 45 publications

Altered Brain Complexity in Women with Primary Dysmenorrhea: A Resting-State Magneto-Encephalography Study Using Multiscale Entropy Analysis

Altered Brain Complexity in Women with Primary Dysmenorrhea: A Resting-State Magneto-Encephalography Study Using Multiscale Entropy Analysis

Refined Multiscale Entropy Using Fuzzy Metrics: Validation and Application to Nociception Assessment

Interactions of BDNF Val66Met Polymorphism and Menstrual Pain on Brain Complexity

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