Multifractal detrended fluctuation analysis based novel feature extraction technique for automated detection of focal and non‐focal electroencephalogram signals

Chatterjee, Soumya; Pratiher, Sawon; Bose, Rohit

doi:10.1049/iet-smt.2017.0117

Cited by 49 publications

(40 citation statements)

References 21 publications

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“…Despite this, we obtained pretty good accuracy results. The 95.5% classification accuracy obtained in this work surpasses many other related works [13,[15][16][17][18][20][21][22]47], while some other works [19,23,24,48] surpasses this result by a maximum of 1.5% classification accuracy ( [19] achieved 97% classification accuracy). Thus, when not considering [25], the comparison with the other related works can be considered pretty good in light of the great simplification achieved for the feature extraction process.…”

Section: Discussionsupporting

confidence: 43%

“…Another work of Bhattacharyya et al [15] also used multivariate subband entropy measures from TQWT along with multivariate fuzzy entropy in combination with a LS-SVM classifier model. Chatterjee et al [21] also used SVM and k-NN classifiers fed by multifractal, detrended fluctuation analysis (MFDFA) based feature sets. Singh et al [22] used features derived from DFT-based rhythms of the EEG to fed the LS-SVM classifier.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation

et al. 2020

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The epileptogenic focus is a brain area that may be surgically removed to control of epileptic seizures. Locating it is an essential and crucial step prior to the surgical treatment. However, given the difficulty of determining the localization of this brain region responsible of the initial seizure discharge, many works have proposed machine learning methods for the automatic classification of focal and non-focal electroencephalographic (EEG) signals. These works use automatic classification as an analysis tool for helping neurosurgeons to identify focal areas off-line, out of surgery, during the processing of the huge amount of information collected during several days of patient monitoring. In turn, this paper proposes an automatic classification procedure capable of assisting neurosurgeons online, during the resective epilepsy surgery, to refine the localization of the epileptogenic area to be resected, if they have doubts. This goal requires a real-time implementation with as low a computational cost as possible. For that reason, this work proposes both a feature set and a classifier model that minimizes the computational load while preserving the classification accuracy at 95.5%, a level similar to previous works. In addition, the classification procedure has been implemented on a FPGA device to determine its resource needs and throughput. Thus, it can be concluded that such a device can embed the whole classification process, from accepting raw signals to the delivery of the classification results in a cost-effective Xilinx Spartan-6 FPGA device. This real-time implementation begins providing results after a 5 s latency, and later, can deliver floating-point classification results at 3.5 Hz rate, using overlapped time-windows.

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Section: Discussionsupporting

confidence: 43%

Section: Introductionmentioning

confidence: 99%

Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation

et al. 2020

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“…MXE and MNE refer to the maximum and minimum values of the singularity exponent in the spectrum. 36 It relates to smallest and largest fluctuations of the time series, respectively. PEV refers to the singularity exponent, where f(a) has its peak value.…”

Section: Feature Extractionmentioning

confidence: 99%

Multifractal analysis of uterine electromyography signals to differentiate term and preterm conditions

Punitha

Swaminathan

2019

Proc Inst Mech Eng H

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In this study, an attempt has been made to identify the origin of multifractality in uterine electromyography signals and to differentiate term (gestational age. 37 weeks) and preterm (gestational age 4 37 weeks) conditions by multifractal detrended moving average technique. The signals obtained from a publicly available database, recorded from the abdominal surface during the second trimester, are used in this study. The signals are preprocessed and converted to shuffle and surrogate series to examine the source of multifractality. Multifractal detrended moving average algorithm is applied on all the signals. The presence of multifractality is verified using scaling exponents, and multifractal spectral features are extracted from the spectrum. The variation of multifractal features in term and preterm conditions is analyzed statistically using Student's t-test. The results of scaling exponents show that the uterine electromyography or electrohysterography signals reveal multifractal characteristics in term and preterm conditions. Further investigation indicates the existence of long-range correlation as the primary source of multifractality. Among all extracted features, strength of multifractality, exponent index, and maximum and peak singularity exponents are statistically significant (p \ 0.05) in differentiating term and preterm conditions. The coefficient of variation is found to be lower for strength of multifractality and peak singularity exponent, which reveal that these features exhibit less inter-subject variance. Hence, it appears that multifractal analysis can aid in the diagnosis of preterm or term delivery of pregnant women.

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“…Using fluctuation functions of a different order, the scaling behaviour of a non-linear time series can be analysed in different segments. MFDFA has been applied successfully to study the non-linear and chaotic nature of various time series like partial discharge signals in power systems [26] and also for analysis of several physiological signals including EEG [27,28]. The novelty of the proposed study is that this contribution not only deals with the non-linear signal analysis using MFDFA, but also classification of EEG signals based on several new features (extracted from multifractal spectrum (MS) of EEG signals) is also presented here, which has been hardly reported in any existing literatures.…”

Section: Introductionmentioning

confidence: 99%

Detection of epileptic seizure employing a novel set of features extracted from multifractal spectrum of electroencephalogram signals

Bose

Pratiher²,

Chatterjee

2019

IET signal process.

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Here, a technique for automated detection of epilepsy is proposed, based on a novel set of features derived from the multifractal spectrum of electroencephalogram (EEG) signals. In fractal geometry, multifractal detrended fluctuation analysis (MDFA) is a technique to examine the self-similarity of a non-linear, chaotic and noisy time series. EEG signals which are representatives of complex human brain dynamics can be effectively characterised by MDFA. Here, EEG signals representing healthy, interictal and seizure activities are acquired from an available dataset. The acquired signals are at first analysed using MDFA. Based on the multifractal analysis, 14 novel features are proposed in this study, to distinguish between different types of EEG signals. The statistical significance of the selected features is evaluated using Kruskal-Wallis test and is finally served as input feature vector to a support vector machines classifier for the classification of EEG signals. Four classification problems are presented in this work and it is observed that 100% classification accuracy is obtained for three problems which validate the efficacy of the proposed model for computer-aided diagnosis of epilepsy.

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Multifractal detrended fluctuation analysis based novel feature extraction technique for automated detection of focal and non‐focal electroencephalogram signals

Cited by 49 publications

References 21 publications

Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation

Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation

Multifractal analysis of uterine electromyography signals to differentiate term and preterm conditions

Detection of epileptic seizure employing a novel set of features extracted from multifractal spectrum of electroencephalogram signals

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