Multiscale Entropy Analysis of EEG for Assessment of Post-Cardiac Arrest Neurological Recovery Under Hypothermia in Rats

Kang, Xiaoxu; Jia, Xiaofeng; Geocadin, Romergryko G.; Thakor, Nitish V.; Maybhate, Anil

doi:10.1109/tbme.2008.2011917

Cited by 44 publications

(23 citation statements)

References 48 publications

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“…In this study, we have used threshold-based symbolic entropy to distinguish epileptic and healthy subjects as well as eye-open and eye-closed conditions. The results of the study were compared to those of the MSE [33], which has been used in several neurological studies to differentiate hypothermia and normothermia conditions [39], describe dynamical alternation owing to Alzheimer disease and dynamics of seizure-free, pre-seizure and during seizure states [41]. …”

Section: Discussionmentioning

confidence: 99%

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Symbolic time series analysis of electroencephalographic (EEG) epileptic seizure and brain dynamics with eye-open and eye-closed subjects during resting states

Hussain

Aziz

Alowibdi

et al. 2017

J Physiol Anthropol

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ObjectiveEpilepsy is a neuronal disorder for which the electrical discharge in the brain is synchronized, abnormal and excessive. To detect the epileptic seizures and to analyse brain activities during different mental states, various methods in non-linear dynamics have been proposed. This study is an attempt to quantify the complexity of control and epileptic subject with and without seizure as well as to distinguish eye-open (EO) and eye-closed (EC) conditions using threshold-based symbolic entropy.MethodsThe threshold-dependent symbolic entropy was applied to distinguish the healthy and epileptic subjects with seizure and seizure-free intervals (i.e. interictal and ictal) as well as to distinguish EO and EC conditions. The original time series data was converted into symbol sequences using quantization level, and word series of symbol sequences was generated using a word length of three or more. Then, normalized corrected Shannon entropy (NCSE) was computed to quantify the complexity. The NCSE values were not following the normal distribution, and the non-parametric Mann–Whitney–Wilcoxon (MWW) test was used to find significant differences among various groups at 0.05 significance level. The values of NCSE were presented in a form of topographic maps to show significant brain regions during EC and EO conditions. The results of the study were compared to those of the multiscale entropy (MSE).ResultsThe results indicated that the dynamics of healthy subjects are more complex compared to epileptic subjects (during seizure and seizure-free intervals) in both EO and EC conditions. The comparison of the dynamics of epileptic subjects revealed that seizure-free intervals are more complex than seizure intervals. The dynamics of healthy subjects during EO conditions are more complex compared to those during EC conditions. Further, the results clearly demonstrated that threshold-dependent symbolic entropy outperform MSE in distinguishing different physiological and pathological conditions.ConclusionThe threshold symbolic entropy has provided improved accuracy in quantifying the dynamics of healthy and epileptic subjects during EC an EO conditions for each electrode compared to the MSE.

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Section: Discussionmentioning

confidence: 99%

“…Kang et al . [39] used MSE to track and differentiate dynamical changes in complexity of each sub-band under hypothermia and normothermia conditions. Park et al [40] demonstrated that dynamical alternations owing to Alzheimer disease can be effectively described by MSE curves.…”

Section: Introductionmentioning

confidence: 99%

Symbolic time series analysis of electroencephalographic (EEG) epileptic seizure and brain dynamics with eye-open and eye-closed subjects during resting states

Hussain

Aziz

Alowibdi

et al. 2017

J Physiol Anthropol

View full text Add to dashboard Cite

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“…Therefore, most studies focused on the difference and the correlation of entropy in different conditions. The change of entropy had been used to evaluate neurological recovery in rats under hypothermia treatment [27]. The results indicated that the entropy increases with the treatment time.…”

Section: The General Concept Of Entropy Of Eegmentioning

confidence: 99%

The Potential Application of Multiscale Entropy Analysis of Electroencephalography in Children with Neurological and Neuropsychiatric Disorders

Chu

Chang

Shieh

et al. 2017

Entropy

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Electroencephalography (EEG) is frequently used in functional neurological assessment of children with neurological and neuropsychiatric disorders. Multiscale entropy (MSE) can reveal complexity in both short and long time scales and is more feasible in the analysis of EEG. Entropy-based estimation of EEG complexity is a powerful tool in investigating the underlying disturbances of neural networks of the brain. Most neurological and neuropsychiatric disorders in childhood affect the early stage of brain development. The analysis of EEG complexity may show the influences of different neurological and neuropsychiatric disorders on different regions of the brain during development. This article aims to give a brief summary of current concepts of MSE analysis in pediatric neurological and neuropsychiatric disorders. Studies utilizing MSE or its modifications for investigating neurological and neuropsychiatric disorders in children were reviewed. Abnormal EEG complexity was shown in a variety of childhood neurological and neuropsychiatric diseases, including autism, attention deficit/hyperactivity disorder, Tourette syndrome, and epilepsy in infancy and childhood. MSE has been shown to be a powerful method for analyzing the non-linear anomaly of EEG in childhood neurological diseases. Further studies are needed to show its clinical implications on diagnosis, treatment, and outcome prediction.

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“…This finding was consistent with the study reported by Costa et al [22], where the SampEn of the heart rate time series gradually increased from Scale 1 to Scale 6 for healthy young and old individuals. Kang et al [27] observed an increased SampEn of EEG signals from Scale 1 to Scale 20 in rats, and it has been suggested that signals at larger scales could produce a higher entropy (FuzzyEn/SampEn) due to a shorter data length [28,31].…”

Section: Msfuzzyen Analysis Of Trajectories and Emg Signalsmentioning

confidence: 99%

“…To avoid misleading results, multiscale entropy was developed by Costa et al to detect complex variations of the physiological system across multiple time scales [22], which was combined with a coarse-graining procedure and SampEn. Multiscale entropy and its variants have been applied in the detection of muscle fatigue [23], gait dynamics analysis [24,25], heart rate dynamics analysis [21,26], and electroencephalogram (EEG) analysis in rats [27]. Previous studies confirmed that studying the physiological system over a range of timescales could allow for more complete features [24].…”

Section: Introductionmentioning

confidence: 99%

Effects of Task Demands on Kinematics and EMG Signals during Tracking Tasks Using Multiscale Entropy

Song

2017

Entropy

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Target-directed elbow movements are essential in daily life; however, how different task demands affect motor control is seldom reported. In this study, the relationship between task demands and the complexity of kinematics and electromyographic (EMG) signals on healthy young individuals was investigated. Tracking tasks with four levels of task demands were designed, and participants were instructed to track the target trajectories by extending or flexing their elbow joint. The actual trajectories and EMG signals from the biceps and triceps were recorded simultaneously. Multiscale fuzzy entropy was utilized to analyze the complexity of actual trajectories and EMG signals over multiple time scales. Results showed that the complexity of actual trajectories and EMG signals increased when task demands increased. As the time scale increased, there was a monotonic rise in the complexity of actual trajectories, while the complexity of EMG signals rose first, and then fell. Noise abatement may account for the decreasing entropy of EMG signals at larger time scales. This study confirmed the uniqueness of multiscale entropy, which may be useful in the analysis of electrophysiological signals.

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Multiscale Entropy Analysis of EEG for Assessment of Post-Cardiac Arrest Neurological Recovery Under Hypothermia in Rats

Cited by 44 publications

References 48 publications

Symbolic time series analysis of electroencephalographic (EEG) epileptic seizure and brain dynamics with eye-open and eye-closed subjects during resting states

Symbolic time series analysis of electroencephalographic (EEG) epileptic seizure and brain dynamics with eye-open and eye-closed subjects during resting states

The Potential Application of Multiscale Entropy Analysis of Electroencephalography in Children with Neurological and Neuropsychiatric Disorders

Effects of Task Demands on Kinematics and EMG Signals during Tracking Tasks Using Multiscale Entropy

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