Time-Shift Multiscale Entropy Analysis of Physiological Signals

Pham, Tuan D.

doi:10.3390/e19060257

Cited by 39 publications

(34 citation statements)

References 30 publications

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“…Entropy not only can be used to measure the additional information needed to determine the state of a system, but also can be used to quantify the irregular, random and chaotic behavior of physiological signals [12]. ere have been reported many application cases of entropy-based pattern learning by the use of a variety of entropy measures (such as approximate entropy (ApEn), sample entropy (SampEn), permutation entropy, spectral entropy, short-term Rényi entropy and Shannon entropy, and so on) [13][14][15][16][17][18][19][20][21]. For instance, Raghu et al proposed a novel minimum variance modi ed fuzzy entropy to identify epileptic seizures in real time from electroencephalogram (EEG) signals, which achieved the classi cation accuracy of 100% [22].…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals

Wang

et al. 2020

Complexity

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Measures of predictability in physiological signals based on entropy metrics have been widely used in the application domain of medical assessment and clinical diagnosis. In this paper, we propose a new entropy-based pattern learning by a combination of singular spectrum analysis (SSA) and entropy measures for assessment of physiological signals. Physiological signals are first represented as a series of SSA components, and then well-established entropy measures are extracted from the resulting SSA components that can help to facilitate the features extraction from physiological signals. The entropy measures of notable SSA components are used to form input features and fed into pattern classifier. To demonstrate its validity, applicability, and versatility, the proposed entropy-based pattern learning is used to perform medical assessments with three kinds of classical physiological signals, that is, electroencephalogram (EEG), electromyogram (EMG), and RR-interval signals. Experiments demonstrate that in all cases, the proposed entropy-based pattern learning can effectively capture specific biosignal patterns of physiological signals and achieve excellent identification performances for the assessments of EEG, EMG, and RR-interval signals. Besides, through the comparison of the identification performances for entropy-based pattern learning based on the physiological signals themselves and the SSA components, it is concluded that the discriminating power of entropy-based pattern learning based on the SSA components is much stronger than that based on the physiological signals themselves. Since it can be easily extended to any other physiological signal analysis, the proposed entropy-based pattern learning may use as an efficient approach to reveal biosignal patterns for medical assessment of physiological signals.

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Section: Introductionmentioning

confidence: 99%

Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals

Wang

et al. 2020

Complexity

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“…Many modified versions of MSE have been developed to describe the multiscale properties of time series [32], such as composite multiscale entropy [33] and time-shift multiscale entropy (TSME) [34]. TSME was proposed by Pham [34], modifying the MSE over time series X of length N, with multiple time shifts at a given time interval kmax.…”

Section: Multiscale Entropy (Mse) and Time-shift Multiscale Entropy (mentioning

confidence: 99%

“…TSME was proposed by Pham [34], modifying the MSE over time series X of length N, with multiple time shifts at a given time interval kmax. The TSME is computed in the following three steps.…”

Section: Multiscale Entropy (Mse) and Time-shift Multiscale Entropy (mentioning

confidence: 99%

“…The TSME was proposed by Pham in 2017 [34] and the author has opened the original codes related to computation of TSME in his personal homepage [39]. The EEG signals were from the EEGMMID, representing the first set of experimental data of the subjects labeled from S001 to S010 corresponding to electrode name Fz (numbering 34 in the records) as shown in Figure 13.…”

Section: Accelerating Computations Of Time-shift Multiscale Entropy (mentioning

confidence: 99%

“…Firstly, for a given time interval kmax, the k time-shift series is constructed from time series X by means of the known Higuchi's fractal dimension [34,35]. Then, either the SpEn or ApEn is computed for all time-shift series, denoted as TSME β k , β = 1, 2, ..., k. Finally, the TSME for each k is defined as the mean value of all TSME β k where k = 1, 2, ..., kmax [34]. With increasing k, the SpEn-based TSME β k can remain fairly constant, while the ApEn-based TSME β k tends to slightly decrease.…”

Section: Multiscale Entropy (Mse) and Time-shift Multiscale Entropy (mentioning

confidence: 99%

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Accelerating the Computation of Entropy Measures by Exploiting Vectors with Dissimilarity

Wang

Peng

et al. 2017

Entropy

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Abstract:In the diagnosis of neurological diseases and assessment of brain function, entropy measures for quantifying electroencephalogram (EEG) signals are attracting ever-increasing attention worldwide. However, some entropy measures, such as approximate entropy (ApEn), sample entropy (SpEn), multiscale entropy and so on, imply high computational costs because their computations are based on hundreds of data points. In this paper, we propose an effective and practical method to accelerate the computation of these entropy measures by exploiting vectors with dissimilarity (VDS). By means of the VDS decision, distance calculations of most dissimilar vectors can be avoided during computation. The experimental results show that, compared with the conventional method, the proposed VDS method enables a reduction of the average computation time of SpEn in random signals and EEG signals by 78.5% and 78.9%, respectively. The computation times are consistently reduced by about 80.1~82.8% for five kinds of EEG signals of different lengths. The experiments further demonstrate the use of the VDS method not only to accelerate the computation of SpEn in electromyography and electrocardiogram signals but also to accelerate the computations of time-shift multiscale entropy and ApEn in EEG signals. All results indicate that the VDS method is a powerful strategy for accelerating the computation of entropy measures and has promising application potential in the field of biomedical informatics.

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Entropy Algorithms

Pham¹

2020

Fuzzy Recurrence Plots and Networks With Applications in Biomedicine

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Time-Shift Multiscale Entropy Analysis of Physiological Signals

Cited by 39 publications

References 30 publications

Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals

Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals

Accelerating the Computation of Entropy Measures by Exploiting Vectors with Dissimilarity

Entropy Algorithms

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