Classification of Normal and Pre-Ictal EEG Signals Using Permutation Entropies and a Generalized Linear Model as a Classifier

Redelico, Francisco O.; Traversaro, Francisco; García, María del Carmen García; Silva, Walter; Rosso, Osvaldo A.; Risk, Marcelo

doi:10.3390/e19020072

Cited by 26 publications

(21 citation statements)

References 39 publications

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“…The classification of the EEG records from a very well known database by the scientific community working on this field follows the same pattern. Although the experiments are not exactly the same, the results achieved for the full length records (4097 samples) are very similar to those in [ 55 ], and in [ 54 ], among other papers, with AUCs in the 0.95 range for the specific classes compared. However, as demonstrated in Table 9 , a significant separability is achieved for as few as 100 samples, and for any m between 3 and 7.…”

Section: Discussionsupporting

confidence: 77%

Embedded Dimension and Time Series Length. Practical Influence on Permutation Entropy and Its Applications

Cuesta–Frau

Murillo-Escobar

Orrego

et al. 2019

Entropy

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Permutation Entropy (PE) is a time series complexity measure commonly used in a variety of contexts, with medicine being the prime example. In its general form, it requires three input parameters for its calculation: time series length N, embedded dimension m, and embedded delay τ . Inappropriate choices of these parameters may potentially lead to incorrect interpretations. However, there are no specific guidelines for an optimal selection of N, m, or τ , only general recommendations such as N > > m ! , τ = 1 , or m = 3 , … , 7 . This paper deals specifically with the study of the practical implications of N > > m ! , since long time series are often not available, or non-stationary, and other preliminary results suggest that low N values do not necessarily invalidate PE usefulness. Our study analyses the PE variation as a function of the series length N and embedded dimension m in the context of a diverse experimental set, both synthetic (random, spikes, or logistic model time series) and real–world (climatology, seismic, financial, or biomedical time series), and the classification performance achieved with varying N and m. The results seem to indicate that shorter lengths than those suggested by N > > m ! are sufficient for a stable PE calculation, and even very short time series can be robustly classified based on PE measurements before the stability point is reached. This may be due to the fact that there are forbidden patterns in chaotic time series, not all the patterns are equally informative, and differences among classes are already apparent at very short lengths.

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Section: Discussionsupporting

confidence: 77%

Embedded Dimension and Time Series Length. Practical Influence on Permutation Entropy and Its Applications

Cuesta–Frau

Murillo-Escobar

Orrego

et al. 2019

Entropy

View full text Add to dashboard Cite

show abstract

“…Up to now, there have been emerging entropy algorithms to quantify the uncertainty of time sequence, such as approximate entropy (AE) [ 13 ], sample entropy (SE) [ 14 ] and permutation entropy (PE) [ 15 ]. As PE enjoys the merits of being conceptually simple and computationally fast, since proposed, it has been widely used in the fields of EEG signal research [ 16 ], financial sequence analysis [ 17 ] and mechanical fault diagnosis [ 18 ]. A study has proven the better stability of PE than that of AE and SE [ 19 ], but PE only compares the neighboring values without consideration of amplitude, meanwhile, it is also greatly influenced by the equal value, which results in a deviation between the calculated value and the true value.…”

Section: Introductionmentioning

confidence: 99%

A New Feature Extraction Method Based on Improved Variational Mode Decomposition, Normalized Maximal Information Coefficient and Permutation Entropy for Ship-Radiated Noise

Xie

Sun

2020

Entropy

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Due to the existence of marine environmental noise, coupled with the instability of underwater acoustic channel, ship-radiated noise (SRN) signals detected by sensors tend to suffer noise pollution as well as distortion caused by the transmission medium, making the denoising of the raw detected signals the new focus in the field of underwater acoustic target recognition. In view of this, this paper presents a novel hybrid feature extraction scheme integrating improved variational mode decomposition (IVMD), normalized maximal information coefficient (norMIC) and permutation entropy (PE) for SRN signals. Firstly, the IVMD method is employed to decompose the SRN signals into a number of finite intrinsic mode functions (IMFs). The noise IMFs are then filtered out by a denoising method before PE extraction. Next, the MIC between each retained IMF and the raw SRN signal and PE of retained IMFs are calculated, respectively. After this, the norMICs are used to weigh the PE values of the retained IMFs and the sum of the weighted PE results is regarded as the classification parameter. Finally, the feature vectors are fed into the particle swarm optimization-based support vector machine multi-class classifier (PSO-SVM) to identify different types of SRN samples. The experimental results have indicated that the classification accuracy of the proposed method is as high as 99.1667%, which is much higher than that of other currently existing methods. Hence, the method proposed in this paper is more suitable for feature extraction of SRN signals in practical application.

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“…An epileptic seizure is one of those complex abnormalities detected by EEG. A vast number of methods have been developed for automatic detection of seizures from EEG recordings (see [3][4][5]). Extracting features that best describe the behavior of EEGs is of great importance for automatic seizure detection systems' performance.…”

Section: Introductionmentioning

confidence: 99%

Pattern Recognition in Epileptic EEG Signals via Dynamic Mode Decomposition

et al. 2020

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In this paper, we propose a new method based on the dynamic mode decomposition (DMD) to find a distinctive contrast between the ictal and interictal patterns in epileptic electroencephalography (EEG) data. The features extracted from the method of DMD clearly capture the phase transition of a specific frequency among the channels corresponding to the ictal state and the channel corresponding to the interictal state, such as direct current shift (DC-shift or ictal slow shifts) and high-frequency oscillation (HFO). By performing classification tests with Electrocorticography (ECoG) recordings of one patient measured at different timings, it is shown that the captured phenomenon is the unique pattern that occurs in the ictal onset zone of the patient. We eventually explain how advantageously the DMD captures some specific characteristics to distinguish the ictal state and the interictal state. The method presented in this study allows simultaneous interpretation of changes in the channel correlation and particular information for activity related to an epileptic seizure so that it can be applied to identification and prediction of the ictal state and analysis of the mechanism on its dynamics.

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Classification of Normal and Pre-Ictal EEG Signals Using Permutation Entropies and a Generalized Linear Model as a Classifier

Cited by 26 publications

References 39 publications

Embedded Dimension and Time Series Length. Practical Influence on Permutation Entropy and Its Applications

Embedded Dimension and Time Series Length. Practical Influence on Permutation Entropy and Its Applications

A New Feature Extraction Method Based on Improved Variational Mode Decomposition, Normalized Maximal Information Coefficient and Permutation Entropy for Ship-Radiated Noise

Pattern Recognition in Epileptic EEG Signals via Dynamic Mode Decomposition

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