Comparison of Entropy and Complexity Measures for the Assessment of Depth of Sedation

Ferenets, Rain; Lipping, Tarmo; Anier, Andres; Jäntti, V.; Melto, S.; Hovilehto, S.

doi:10.1109/tbme.2006.873543

Cited by 235 publications

(172 citation statements)

References 23 publications

Supporting

Mentioning

164

Contrasting

Unclassified

Order By: Relevance

“…It assigns larger values to more complex data [30]. This non-linear feature can offer information about the electromagnetic brain dynamics taking into account that it mainly depends on the signal bandwidth and, to a smaller degree, on the sequence probability density function [29,31].…”

Section: Lempel-ziv Complexity (Lzc)mentioning

confidence: 99%

See 1 more Smart Citation

Blind source separation to enhance spectral and non-linear features of magnetoencephalogram recordings. Application to Alzheimer's disease

Escudero¹,

Hornero²,

Abásolo³

et al. 2009

Medical Engineering & Physics

View full text Add to dashboard Cite

This work studied whether a blind source separation (BSS) and component selection procedure could increase the differences between Alzheimer's disease (AD) patients and control subjects' spectral and non-linear features of magnetoencephalogram (MEG) recordings. MEGs were acquired with a 148-channel whole-head magnetometer from 62 subjects (36 AD patients and 26 controls), who were divided randomly into training and test sets. MEGs were decomposed using the algorithm for multiple unknown signals extraction (AMUSE). The extracted AMUSE components were characterised with two spectral -median frequency and spectral entropy (SpecEn) -and two non-linear features: Lempel-Ziv complexity (LZC) and sample entropy (SampEn). One-way analyses of variance with age as a covariate were applied to the training set to decide which components had the most significant differences between groups. Then, partial reconstructions of the MEGs were computed with these significant components. In the test set, the accuracy and area under the ROC curve (AUC) associated with each partial reconstruction of the MEGs were compared with the case where no BSS-preprocessing was applied. This preprocessing increased the AUCs between 0.013 and 0.227, while the accuracy for SpecEn, LZC and SampEn rose between 6.4% and 22.6%, improving the separation between AD patients and control subjects.

show abstract

Section: Lempel-ziv Complexity (Lzc)mentioning

confidence: 99%

“…In this study, this transformation has been performed by comparing the data points with the median of the signal. The algorithm to compute the LZC can be found in [29] or [31].…”

Section: Lempel-ziv Complexity (Lzc)mentioning

confidence: 99%

Blind source separation to enhance spectral and non-linear features of magnetoencephalogram recordings. Application to Alzheimer's disease

Escudero¹,

Hornero²,

Abásolo³

et al. 2009

Medical Engineering & Physics

View full text Add to dashboard Cite

show abstract

“…In this study, of each moving non-overlapping window with a length of 1 second is computed as a feature. Also, according to reference [42] and . Standard variation, Variance and Energy of auto-covariance: standard variation, variance and energy of the auto-covariance of each segment.…”

Section: Kurtosismentioning

confidence: 99%

Patient-specific epileptic seizure detection in long-term EEG recording in paediatric patients with intractable seizures

Zabihi

Kıranyaz

İnce

et al. 2013

IET Intelligent Signal Processing Conference 2013 (ISP 2013)

View full text Add to dashboard Cite

Over recent years, due to the increase in the epileptic patient population, issues of diagnosing and treatment of epilepsy have become more and more prominent and much research has been done in this field in consequence. However, there are still many gaps and lack of knowledge in interpreting Electroencephalograph (EEG) signals in order to solve the problem.Particular problems in this area include difficulties in detecting the seizure events (due to the different seizure types and their variability from patient to patient or even in an individual over time), and dealing with long-term EEG recordings, which is an onerous and time consuming task for electroencephalographers.The thesis discusses the two problem areas using EEG data from four subjects with overall 21 hours of recording from the CHB-MIT scalp benchmark EEG dataset. We propose a patient specific seizure detection technique, which selects the optimal feature subsets, and train a dedicated classifier for each patient in order to maximize the classification performance. To exploit the characteristics of a patient's EEG pattern as much as possible, we used a large set of features in the proposed framework, namely time domain, frequency domain, time-frequency domain and nonlinear features, and selected the most crucial features among them by using Conditional Mutual Information Maximization (CMIM) technique. We further performed extensive comparative evaluations against 6 other feature selection methods to demonstrate the superiority of the CMIM.Support Vector Machine (SVM) with the linear kernel is used as the classifier. The experimental results show a delicate classification performance over the test set, i.e. an average of 90.62% sensitivity and 99.32% specificity are acquired when all channels and recordings are used to form a composite feature vector. In addition, an average sensitivity and specificity rates of 93.78% and 99.05% are obtained using CMIM, respectively. PREFACEThis master thesis was carried out in the MUVIS group at the signal processing department of Tampere University of Technology. It was funded by the Academy of Finland.I wish to express my sincere gratitude to Professor Serkan Kiranyaz, whose inspiring guidance and inexhaustible patience have helped me cultivate a systematic approach towards the research. I am indebted to him for the freedom he gave me in implementing my ideas. I have also acquired valuable insights through his instruction in academic approach.I am very much grateful to Professor Turker Ince for his warm support and his valuable suggestions. Besides, I would also like to thank Professor Moncef Gabbouj for providing me with the opportunity for the thesis work. He gave me a lot of trust and flexibility in the project.I have been extremely fortunate in having valuable friends who have made a homely atmosphere for me during my stay in Tampere

show abstract

“…Several complexity measures including Lempel-Ziv complexity [1] and its variants [2] have been used widely to quantify the regularity and extent of randomness in data sampled from physical and biological systems [3][4][5][6][7][8][9][10][11][12][13][14][15]. Such systems are undoubtedly nonlinear feedback systems corrupted with noise at the dynamical ) ( t ∈ and measurement ) ( t η levels.…”

Section: Introductionmentioning

confidence: 99%

“…represented by a schematic diagram [16]. Complexity measures have been successfully used to detect possible non-random structure and discriminate different physiological states of activity [3][4][5][6][7][8][9][10][11][12][13][14][15]. In order to make a finer distinction of the observed non-random signatures, surrogate testing [17,18] have been used in conjunction with complexity measures [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Interpreting non-random signatures in biomedical signals with Lempel–Ziv complexity

Nagarajan

Szczepański

Wajnryb

2008

Physica D: Nonlinear Phenomena

View full text Add to dashboard Cite

Lempel-Ziv complexity (LZ) [1] and its variants have been used widely to identify nonrandom patterns in biomedical signals obtained across distinct physiological states. Nonrandom signatures of the complexity measure can occur under nonlinear deterministic as well as non-deterministic settings. Surrogate data testing have also been encouraged in the past in conjunction with complexity estimates to make a finer distinction between various classes of processes. In this brief letter, we make two important observations (1) Non-Gaussian noise at the dynamical level can elude existing surrogate algorithms

show abstract

Comparison of Entropy and Complexity Measures for the Assessment of Depth of Sedation

Cited by 235 publications

References 23 publications

Blind source separation to enhance spectral and non-linear features of magnetoencephalogram recordings. Application to Alzheimer's disease

Blind source separation to enhance spectral and non-linear features of magnetoencephalogram recordings. Application to Alzheimer's disease

Patient-specific epileptic seizure detection in long-term EEG recording in paediatric patients with intractable seizures

Interpreting non-random signatures in biomedical signals with Lempel–Ziv complexity

Contact Info

Product

Resources

About