Intrinsic Mode Entropy for Nonlinear Discriminant Analysis

Amoud, Hassan; Snoussi, Hichem; Hewson, David J.; Doussot, Michel; Duchêne, Jacques

doi:10.1109/lsp.2006.888089

Cited by 68 publications

(48 citation statements)

References 9 publications

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“…a) Multivariate MSE Remark 2. Unlike EMD/MEMD based sample entropy methods given in [10] and [11] which employ univariate sample entropy, the proposed method is fully multivariate as it calculates directly multivariate sample entropy estimates, thereby catering for linear/nonlinear correlations both within and between the data channels.…”

Section: Remarkmentioning

confidence: 99%

“…It was recently proposed to employ a data-driven method, the empirical mode decomposition (EMD) [9], to generate intrinsic multiple data scales from input data, to be used for the subsequent MSE analysis [10,11]. The resulting EMDbased MSE method produced improved results owing to the fully data-driven nature of EMD and also due to the fact that it operates locally based on the extrema of the (univariate) in- put signal, yielding well defined narrowband scales intrinsic to the input data.…”

Section: Introductionmentioning

confidence: 99%

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Dynamical complexity of human responses: a multivariate data-adaptive framework

Ahmed¹,

Rehman²,

Looney³

et al. 2012

Bulletin of the Polish Academy of Sciences: Technical Sciences

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Abstract. Established complexity measures typically operate at a single scale and thus fail to quantify inherent long-range correlations in real-world data, a key feature of complex systems. The recently introduced multiscale entropy (MSE) method has the ability to detect fractal correlations and has been used successfully to assess the complexity of univariate data. However, multivariate observations are common in many real-world scenarios and a simultaneous analysis of their structural complexity is a prerequisite for the understanding of the underlying signal-generating mechanism. For this purpose, based on the notion of multivariate sample entropy, the standard MSE method is extended to the multivariate case, whereby for rigor, the intrinsic multivariate scales of the input data are generated adaptively via the multivariate empirical mode decomposition (MEMD) algorithm. This allows us to gain better understanding of the complexity of the underlying multivariate real-world process, together with more degrees of freedom and physical interpretation in the analysis. Simulations on both synthetic and real-world biological multivariate data sets support the analysis.

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Section: Remarkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamical complexity of human responses: a multivariate data-adaptive framework

Ahmed¹,

Rehman²,

Looney³

et al. 2012

Bulletin of the Polish Academy of Sciences: Technical Sciences

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“…In [36], the authors replaced the FIR filter by the low-pass Butterworth filter to rectify the problem of aliasing associated with FIR filter frequency response. As an improvement of MSEn, intrinsic mode entropy (IMEn) [37] is proposed based on EMD, which computes complexity in high frequency scales of the signal, as well as being robust to dominating low-frequency components by combining the group of IMFs. The concept of multilevel filtering has been applied in the EMD domain [38] and in the flexible analytic wavelet transform (FAWT) [39] domain [40].…”

Section: Introductionmentioning

confidence: 99%

Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals

et al. 2017

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This paper analyzes the underlying complexity and non-linearity of electroencephalogram (EEG) signals by computing a novel multi-scale entropy measure for the classification of seizure, seizure-free and normal EEG signals. The quality factor (Q) based multi-scale entropy measure is proposed to compute the entropy of the EEG signal in different frequency-bands of interest. The Q -based entropy (QEn) is computed by decomposing the signal with the tunable-Q wavelet transform (TQWT) into the number of sub-bands and estimating K-nearest neighbor (K-NN) entropies from various sub-bands cumulatively. The optimal selection of Q and the redundancy parameter (R) of TQWT showed better robustness for entropy computation in the presence of high-and low-frequency components. The extracted features are fed to the support vector machine (SVM) classifier with the wrapper-based feature selection method. The proposed method has achieved accuracy of 100% in classifying normal (eyes-open and eyes-closed) and seizure EEG signals, 99.5% in classifying seizure-free EEG signals (from the hippocampal formation of the opposite hemisphere of the brain) from seizure EEG signals and 98% in classifying seizure-free EEG signals (from the epileptogenic zone) from seizure EEG signals, respectively, using the SVM classifier. We have also achieved classification accuracies of 99% and 98.6% in classifying seizure versus non-seizure EEG signals and the individual three classes, namely normal, seizure-free and seizure EEG signals, respectively. The performance measure of the proposed multi-scale entropy has been found to be comparable with the existing state of the art epileptic EEG signals classification methods studied using the same database.

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“…Figure 1 (b) depicts the results of the Shannon entropy, the IMEn and the adaptive subscale entropy. For the IMEn, m = 2 and r = 0.2 are used as recommended in [8]. In the figure, the Shannon entropy is almost constant and the IMEn is not capable of characterizing the underlying components regardless of the distribution and the number of sinusoidal components of the signal.…”

Section: Simulationmentioning

confidence: 99%

“…Due to the potentials of the EMD, it has been increasingly used to analyze nonstationary physiological signals [7]. More recently, intrinsic mode entropy (IMEn) [8] has been developed, which is obtained by calculation of sample entropy of the accumulated sum of IMFs. However, IMEn lays emphasis on fine scales of the underlying time series.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Subscale Entropy Based Quantification of EEG

Choi

2014

IEICE Trans. Inf. & Syst.

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SUMMARYThis letter presents a new entropy measure for electroencephalograms (EEGs), which reflects the underlying dynamics of EEG over multiple time scales. The motivation behind this study is that neurological signals such as EEG possess distinct dynamics over different spectral modes. To deal with the nonlinear and nonstationary nature of EEG, the recently developed empirical mode decomposition (EMD) is incorporated, allowing an EEG to be decomposed into its inherent spectral components, referred to as intrinsic mode functions (IMFs). By calculating Shannon entropy of IMFs in a time-dependent manner and summing them over adaptive multiple scales, the result is an adaptive subscale entropy measure of EEG. Simulation and experimental results show that the proposed entropy properly reveals the dynamical changes over multiple scales.

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Intrinsic Mode Entropy for Nonlinear Discriminant Analysis

Cited by 68 publications

References 9 publications

Dynamical complexity of human responses: a multivariate data-adaptive framework

Dynamical complexity of human responses: a multivariate data-adaptive framework

Tunable-Q Wavelet Transform Based Multiscale Entropy Measure for Automated Classification of Epileptic EEG Signals

Adaptive Subscale Entropy Based Quantification of EEG

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