Detecting Epilepsy in EEG Signals Using Time, Frequency and Time-Frequency Domain Features

Hernández, Daniel E.; Trujillo, Leonardo; Z-Flores, Emigdio; Villanueva, O. M.; Romo-Fewell, O.

doi:10.1007/978-3-319-74060-7_9

Cited by 27 publications

(17 citation statements)

References 29 publications

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“…The time domain features included the signal statistics like power of the signal, mean, standard deviation, etc. First difference and second difference are also computed in time series analysis to get the signal variation over time [ 2 ]. Another time domain feature, namely, Normalized Length Density, was proposed by Jenke et al, which quantifies self-similarities within the EEG signal [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

“…Another time domain feature for EEG analysis was proposed by Hausdorff et al is the Nonstationary Index (NSI) [ 4 ]. The NSI gives a measure of the stationarity of the signal and measures the variation of segments average over time [ 2 ]. Frequency domain analysis includes employing fast Fourier transform (FFT) to calculate power spectrum of signal, relative power of EEG subbands, etc.…”

Section: Introductionmentioning

confidence: 99%

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Diagnosis of Encephalopathy Based on Energies of EEG Subbands Using Discrete Wavelet Transform and Support Vector Machine

Jacob¹,

Nair²,

Iype

et al. 2018

Neurology Research International

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EEG analysis in the field of neurology is customarily done using frequency domain methods like fast Fourier transform. A complex biomedical signal such as EEG is best analysed using a time-frequency algorithm. Wavelet decomposition based analysis is a relatively novel area in EEG analysis and for extracting its subbands. This work aims at exploring the use of discrete wavelet transform for extracting EEG subbands in encephalopathy. The subband energies were then calculated and given as feature sets to SVM classifier for identifying cases of encephalopathy from normal healthy subjects. Out of various combinations of subband energies, energy of delta subband yielded highest performance parameters for SVM classifier with an accuracy of 90.4% in identifying encephalopathy cases.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Diagnosis of Encephalopathy Based on Energies of EEG Subbands Using Discrete Wavelet Transform and Support Vector Machine

Jacob¹,

Nair²,

Iype

et al. 2018

Neurology Research International

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“…Digital signals are subsequently notch-filtered at 50/60 Hz and down-sampled to 128 Hz before broadcast. The EEG signal of each participant are firstly smothered by his own base signal and then bandpass butter worth filter is used on the signal for mining of gamma (30-60 Hz), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), alpha (7-13 Hz), theta (4-7 Hz), and delta (0.5-4 Hz) bands respectively.…”

Section: Eeg Signalsmentioning

confidence: 99%

Cognitive Load Measurement Based on EEG Signals

Tamanna¹,

Parvez²

2021

The Science of Emotional Intelligence

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Measurement of cognitive load should be advantageous in designing an intelligent navigation system for the visually impaired people (VIPs) when navigating unfamiliar indoor environments. Electroencephalogram (EEG) can offer neurophysiological indicators of perceptive process indicated by changes in brain rhythmic activity. To support the cognitive load measurement by means of EEG signals, the complexity of the tasks of the VIPs during navigating unfamiliar indoor environments is quantified considering diverse factors of well-established signal processing and machine learning methods. This chapter describes the measurement of cognitive load based on EEG signals analysis with its existing literatures, background, scopes, features, and machine learning techniques.

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“…Table 1 lists the 39 features considered in this work for each segment of the signal dataset. All these have been used succesfully as features in previous EEG seizure detection works [14,[30][31][32][33][34][35][36][37].…”

Section: Feature Extractionmentioning

confidence: 99%

“…Log energy entropy (LogEn) [30] Non-normalized energy based entropy Median frequency (MDF) [31] Division of the EEG power spectrum into two regions Mean frequency (MNF) [31] Mean normalized frequency of the power spectrum Katz fractal dimension (KFD) [31] Index characterizing the fractal pattern complexity Lower quartile 1 (Q1) [31] 25% of the EEG signal Upper quartile 3 (Q3) [31] 75% of the EEG signal Inter quartile range (IQR) [31] Difference between Q3 and Q1 Semi inter quartile deviation (SID) [31] Measure of spread Skewness (Sk) [32] Measure of the degree of symmetry Kurtosis (Kr) [32] Measure of tailedness of the probability distribution Root mean square (RMS) [33] Root mean square of the EEG signal Band power (PB) [33] Average power of the EEG signal (0 to f s /2) Zero crossing (ZC) [33] Number of times that the signal changes of sign Complexity (Comp) [33] Hjorth parameter Mobility (Mob) [33] Hjorth parameter Activity (Act) [33] Hjorth parameter Spurious free dynamic range (SFDR) [34] Length along a EEG signal Curve length (CL) [34] Length along a EEG signal Teager energy (TE) [34] Non linear energy Variance (Var) [34] Variance of the EEG signal Standard deviation (Std) [34] Standard deviation of the signal Mean (Mean) [34] Mean of the EEG signal 1st derivative variance (Var1) [34] Variance of the first derivative 1st derivative standard deviation (Std1) [34] Standard deviation of the first derivative 1st derivative mean (Mean1) [34] Mean of the first derivative 2nd derivative variance (Var2) [34] Variance of the second derivative 2nd derivative standard deviation (Std2) [34] Standard deviation of the second derivative 2nd derivative mean (Mean2) [34] Mean of the second derivative Derivative variance ratio (RatioVar) [36] Ratio of derivative respect absolute of derivative variances Power (P) [35] Power of the signal window 1st difference (1d)…”

Section: Eeg Feature Descriptionmentioning

confidence: 99%

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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Detecting Epilepsy in EEG Signals Using Time, Frequency and Time-Frequency Domain Features

Cited by 27 publications

References 29 publications

Diagnosis of Encephalopathy Based on Energies of EEG Subbands Using Discrete Wavelet Transform and Support Vector Machine

Diagnosis of Encephalopathy Based on Energies of EEG Subbands Using Discrete Wavelet Transform and Support Vector Machine

Cognitive Load Measurement Based on EEG Signals

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

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