Interpretation of Entropy Algorithms in the Context of Biomedical Signal Analysis and Their Application to EEG Analysis in Epilepsy

Amarantidis, Lampros Chrysovalantis; Abásolo, Daniel

doi:10.3390/e21090840

Cited by 29 publications

(12 citation statements)

References 42 publications

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“…Shannon entropy [26], Rényi entropy [29], and average entropy [28] can be applied globally to all data, or locally only to points around specific points [39]. However, they ignore the temporal order of the patterns in the signal [40].…”

Section: Discussionmentioning

confidence: 99%

“…Permutation entropy [31] and edge permutation entropy [32] use the temporal information [39], but they rely on the occurrence of equal values in the sub-series [41]. Approximate entropy [33] has the advantage of lower computational demand and less effect from noise, but it strongly depends on the time-series length and therefore lacks consistency [40]. Sample entropy [34] is invariant to the time-series length and it performs more consistently under various conditions.…”

Section: Discussionmentioning

confidence: 99%

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Classification of Interbeat Interval Time-Series Using Attention Entropy

Yang

Choudhary

Rahardja

et al. 2023

IEEE Trans. Affective Comput.

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Classification of interbeat interval time-series which fluctuates in an irregular and complex manner is very challenging. Typically, entropy methods are employed to quantify the complexity of the time-series for classifying. Traditional entropy methods focus on the frequency distribution of all the observations in a time-series. This requires a relatively long time-series with at least a couple of thousands of data points, which limits their usages in practical applications. The methods are also sensitive to the parameter settings. In this paper, we propose a conceptually new approach called attention entropy, which pays attention only to the key observations. Instead of counting the frequency of all observations, it analyzes the frequency distribution of the intervals between the key observations in a time-series. The advantages of the attention entropy are that it does not need any parameter to tune, is robust to the time-series length, and requires only linear time to compute. Experiments show that it outperforms fourteen state-of-the-art entropy methods evaluated by real-world datasets. It achieves average classification accuracy of AUC=0.71 while the second-best method, multiscale entropy, achieves AUC=0.62 when classifying four groups of people with a time-series length of 100.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Classification of Interbeat Interval Time-Series Using Attention Entropy

Yang

Choudhary

Rahardja

et al. 2023

IEEE Trans. Affective Comput.

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“…We used mean and variance to capture main characteristics of the signals in the time domain. Furthermore, we calculated entropy, a measure from the information domain to infer the state of the respective ANS subsystem; entropy has previously been suggested to differentiate between ictal and non-ictal segments of intracranial EEG data 15 .…”

Section: Discussionmentioning

confidence: 99%

“…We chose signal entropy as a potential signal marker of interest, since entropy has been shown to increase prior to certain state changes in neural systems before 14 . Related to epilepsy entropy was previously used for example in EEG 15 and ECG 16 data. Entropy was calculated for each segment as H = −p log p, with log denoting the logarithm to base 2 and p being the probability density obtained by binning the data into n bins.…”

Section: Data Recording We Recruited Patients Admitted To the Long-tmentioning

confidence: 99%

Autonomic nervous system changes detected with peripheral sensors in the setting of epileptic seizures

Vieluf

Reinsberger

Atrache

et al. 2020

Sci Rep

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A better understanding of the early detection of seizures is highly desirable as identification of an impending seizure may afford improved treatments, such as antiepileptic drug chronotherapy, or timely warning to patients. While epileptic seizures are known to often manifest also with autonomic nervous system (ANS) changes, it is not clear whether ANS markers, if recorded from a wearable device, are also informative about an impending seizure with statistically significant sensitivity and specificity. Using statistical testing with seizure surrogate data and a unique dataset of continuously recorded multi-day wristband data including electrodermal activity (EDA), temperature (TEMP) and heart rate (HR) from 66 people with epilepsy (9.9 ± 5.8 years; 27 females; 161 seizures) we investigated differences between inter-and preictal periods in terms of mean, variance, and entropy of these signals. We found that signal mean and variance do not differentiate between inter-and preictal periods in a statistically meaningful way. EDA signal entropy was found to be increased prior to seizures in a small subset of patients. Findings may provide novel insights into the pathophysiology of epileptic seizures with respect to ANS function, and, while further validation and investigation of potential causes of the observed changes are needed, indicate that epilepsy-related state changes may be detectable using peripheral wearable devices. Detection of such changes with wearable devices may be more feasible for everyday monitoring than utilizing an electroencephalogram. The current inability to assess when a seizure is most likely to occur constitutes a major burden for people with epilepsy (PWE) 1. From a clinical perspective, this inability precludes the development of better treatments, such as antiepileptic drug chronotherapy adapted to personalized risk profiles, or timely, closed-loop intervention strategies to acutely avert impending seizures 2. Hence, a better understanding of the informative biomarkers underlying the transition to seizures is needed. Most research devoted to the study of seizure onset mechanisms and prior warning signals has traditionally focused on electroencephalogram (EEG). Continuous EEG, however, is impractical for monitoring over extended periods of time, especially when used in the ambulatory setting 3. Wearable devices might offer a promising alternative, as these afford easy-to-use, close monitoring of autonomic nervous system (ANS) function without being invasive or restraining to PWE 4. Alterations of ANS activity are known to occur frequently within multiple domains, such as electrodermal, thermal and cardiac subsystems, in relation to seizures, and show specific patterns across these parameters 5-7. However, further research on these subsystems of the ANS and whether they may afford statistically meaningful identification of preictal periods in terms of sensitivity and specificity is needed.

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“…Recent studies for biological systems offered novel approaches to extract features based on single and multiscale entropy measures in order to achieve high classification accuracy. Especially, extracted features from EEG signal-based entropy helps researchers in the early diagnosis of epilepsy, different types of sleep disorders, and brain-related disorders such as Alzheimers [ 45 ]. Acharya et al [ 46 ] extracted features from EEG signals by using ApEn, SampEn, and Phase Entropies (S1 and S2) for the purpose of detecting epilepsy.…”

Section: Discussionmentioning

confidence: 99%

Classification of Hepatitis Viruses from Sequencing Chromatograms Using Multiscale Permutation Entropy and Support Vector Machines

Öz

Aşkın

2019

Entropy

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Classifying nucleic acid trace files is an important issue in molecular biology researches. For the purpose of obtaining better classification performance, the question of which features are used and what classifier is implemented to best represent the properties of nucleic acid trace files plays a vital role. In this study, different feature extraction methods based on statistical and entropy theory are utilized to discriminate deoxyribonucleic acid chromatograms, and distinguishing their signals visually is almost impossible. Extracted features are used as the input feature set for the classifiers of Support Vector Machines (SVM) with different kernel functions. The proposed framework is applied to a total number of 200 hepatitis nucleic acid trace files which consist of Hepatitis B Virus (HBV) and Hepatitis C Virus (HCV). While the use of statistical-based feature extraction methods allows representing the properties of hepatitis nucleic acid trace files with descriptive measures such as mean, median and standard deviation, entropy-based feature extraction methods including permutation entropy and multiscale permutation entropy enable quantifying the complexity of these files. The results indicate that using statistical and entropy-based features produces exceptionally high performances in terms of accuracies (reached at nearly 99%) in classifying HBV and HCV.

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Interpretation of Entropy Algorithms in the Context of Biomedical Signal Analysis and Their Application to EEG Analysis in Epilepsy

Cited by 29 publications

References 42 publications

Classification of Interbeat Interval Time-Series Using Attention Entropy

Classification of Interbeat Interval Time-Series Using Attention Entropy

Autonomic nervous system changes detected with peripheral sensors in the setting of epileptic seizures

Classification of Hepatitis Viruses from Sequencing Chromatograms Using Multiscale Permutation Entropy and Support Vector Machines

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