Analysis of spike waves in epilepsy using Hilbert-Huang transform

Abstract

In this paper, we used the Hilbert-Huang transform (HHT) analysis method to examine the time-frequency characteristics of spike waves for detecting epilepsy symptoms. We obtained a sample of spike waves and nonspike waves for HHT decomposition by using numerous intrinsic mode functions (IMFs) of the Hilbert transform (HT) to determine the instantaneous, marginal, and Hilbert energy spectra. The Pearson correlation coefficients of the IMFs, and energy-IMF distributions for the electroencephalogram (EEG) signal without spike waves, Spike I, Spike II and Spike III sample waves were determined. The analysis results showed that the ratios of the referred wave and Spike III wave to the referred total energy for IMF1, IMF2, and the residual function exceeded 10%. Furthermore, the energy ratios for IMF1, IMF2, IMF3 and the residual function of Spike I, Spike II to their total energy exceeded 10%. The Pearson correlation coefficients of the IMF3 of the EEG signal without spike waves and Spike I wave, EEG signal without spike waves and Spike II wave, EEG signal without spike waves and Spike III wave, Spike I and II waves, Spike I and III waves, and Spike II and III waves were 0.002, 0.06, 0.01, 0.17, 0.03, and 0.3, respectively. The energy ratios of IMF3 in the δ band to its referred total energy for the EEG signal without spike waves, and of the Spike I, II, and III waves were 4.72, 6.75, 5.41, and 5.55%, respectively. The weighted average frequency of the IMF1, IMF2, and IMF3 of the EEG signal without spike waves was lower than that of the IMF1, IMF2, and IMF3 of the spike waves, respectively. The weighted average magnitude of the IMF3, IMF4, and IMF5 of the EEG signal without spike waves was lower than that of the IMF1, IMF2, and IMF3 of spike waves, respectively.

Comparison between traditional FFT and marginal spectra using the Hilbert-Huang transform method for the broadband spectral analysis of the EEG in healthy humans

“…Even though the HMS has been used by other authors for particular applications on EEG spectral analysis (Chen et al 2010;Chen et al 2016;Li 2006;Li et al 2008;Xiangjun et al 2017;Zhu et al 2015;Park et al 2011) the possible differences with the traditional FFT spectrum have not been analyzed in details particularly in healthy humans, as we have described in this study, and could be considered the first report about this particular issue. The fact that we have shown no substantial differences for the typical indices calculated for broadband EEG spectral analysis using both methods in this study, can be considered a good evidence that the use of the classic FFT method, applied extensively and for many years, can be used in the conditions in which was carried out this investigation, it is, in healthy subjects, during a functional state of relaxed wakefulness during resting eyes-closed condition, and for segments of consecutive EEG of 60 seconds, but for other physiological conditions, during cognitive studies, or to study patients with different pathologies that can affect the processes responsible of the generation of the bioelectric activity of the brain, the presence of nonlinearity and particularly of non-stationarity may produce a misleading result while using the FFT methods (Alegre-Cortes et al 2016;Huang et al 1998;Munoz-Gutierrez et al 2018;Soler et al 2020;Tsai et al 2016).…”

“…The HMS has been used by different authors after applying the Hilbert-Huang method to the EEG signal (Chen et al 2010;Li 2006;Li et al 2008;Chen et al 2016;Xiangjun et al 2017;Zhu et al 2015;Park et al 2011) but to our knowledge, the possible differences between the results obtained using the HMS and the conventional FFT spectra in resting conditions in healthy humans have not been studied in detail, and it results imperative, because for many years the universally used method for the spectral analysis of the EEG has used the FFT spectra. The advantages and possible limitations of the novel alternative approach, in this case the use of the HMS, could be better appreciated and could contribute to their more extended use in EEG investigations.…”

Epilepsy seizure onset detection applying 1-NN classifier based on statistical parameters

“…This distribution was recently applied in spike-and-wave pattern recognition in epileptic signals [5]. Based on the results of this study and [9], [11], [12], the following question arose: what if this distribution could be used to detect a seizure onset in epileptic EEG signals?…”

Statistical Model-Based Classification to Detect Patient-Specific Spike-and-Wave in EEG Signals

“…Table 1 lists some more methods. Other existing methods implement signal analysis techniques, such as Hilbert-Huang transform to analyze time-frequency energy distribution [34], complex network of neuronal oscillators to model SWD [35], analyzing statistical features such as variance, the sum of wave amplitudes, slope of the wave [36], or topographic cluster analysis based on connectivity, entropy, frequency, power, and spike amplitude [37]. For a biological dynamic explanation of features and mechanisms generating SWD in the brain see [38].…”