Chaotic Analysis of Epileptic EEG Signals

Kannathal, N.; Chee, Johnny; Kenneth, Er.; Lim, Karen; Tat, Ong Hian

doi:10.1007/978-3-319-02913-9_166

Cited by 12 publications

(10 citation statements)

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“…Some groups have attempted to quantitatively describe the underlying functional and neuronal network that facilitates hypsarrhythmia through EEG-fMRI (Siniatchkin et al 2007), source analysis methods (Japaridze et al 2013), and detection of fast oscillations (Kobayashi et al 2015). Though the hypsarrhythmia signal is often empirically described as “chaotic,” with the term describing the signal’s disorganized appearance (Pavone et al 2013), the mathematical definition of chaos and signal nonlinearity has been explored in several forms of epilepsy (Babloyantz and Destexhe 1986; Van Putten and Stam 2001; Kannathal et al 2014). In hypsarrhythmia, an inter-ictal phenomenon, the deviation from stochastic behavior was greater than in control data, but not as nonlinear as seen during seizure periods (Van Putten and Stam 2001).…”

Section: Discussionmentioning

confidence: 99%

Long-Range Temporal Correlations Reflect Treatment Response in the Electroencephalogram of Patients with Infantile Spasms

et al. 2017

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Infantile spasms syndrome is an epileptic encephalopathy in which prompt diagnosis and treatment initiation are critical to therapeutic response. Diagnosis of the disease heavily depends on the identification of characteristic electroencephalographic (EEG) patterns, including hypsarrhythmia. However, visual assessment of the presence and characteristics of hypsarrhythmia is challenging because multiple variants of the pattern exist, leading to poor inter-rater reliability. We investigated whether a quantitative measurement of the control of neural synchrony in the EEGs of infantile spasms patients could be used to reliably distinguish the presence of hypsarrhythmia and indicate successful treatment outcomes. We used autocorrelation and Detrended Fluctuation Analysis (DFA) to measure the strength of long-range temporal correlations in 21 infantile spasms patients before and after treatment and 21 control subjects. The strength of long-range temporal correlations was significantly lower in patients with hypsarrhythmia than control patients, indicating decreased control of neural synchrony. There was no difference between patients without hypsarrhythmia and control patients. Further, the presence of hypsarrhythmia could be classified based on the DFA exponent and intercept with 92% accuracy using a support vector machine. Successful treatment was marked by a larger increase in the DFA exponent compared to those in which spasms persisted. These results suggest that the strength of long-range temporal correlations is a marker of pathological cortical activity that correlates with treatment response. Combined with current clinical measures, this quantitative tool has the potential to aid objective identification of hypsarrhythmia and assessment of treatment efficacy to inform clinical decision-making.

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Section: Discussionmentioning

confidence: 99%

Long-Range Temporal Correlations Reflect Treatment Response in the Electroencephalogram of Patients with Infantile Spasms

et al. 2017

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“…Entropy. Shannon entropy has been reported to be lower in epilepsy patients than healthy subjects (Kannathal et al 2005a(Kannathal et al , 2005b(Kannathal et al , 2014Rosso et al 2005). From a nonlinear dynamics perspective, this is because epileptic data often exhibits a lower dimension than healthy data, which is more stochastic in nature.…”

Section: Discussionmentioning

confidence: 99%

“…We calculated the optimal number of bins according to Freedman andDiaconis, 1981 (Freedman andDiaconis 1981) as described by Cohen, 2014(Cohen 2014. The entropy calculation has units of bits, and higher values indicate more stochastic behavior (Van Putten and Stam 2001;Kannathal et al 2005aKannathal et al , 2005bKannathal et al , 2014Rosso et al 2005). We calculated Shannon entropy values for all EEG electrodes and reported the mean entropy value.…”

Section: Entropymentioning

confidence: 99%

Computational characteristics of interictal EEG as objective markers of epileptic spasms

Smith¹,

Hu²,

Shrey

et al. 2020

Preprint

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ObjectiveFavorable neurodevelopmental outcomes in epileptic spasms (ES) are tied to early diagnosis and prompt treatment, but uncertainty in the identification of the disease can delay this process. Therefore, we investigated five computational electroencephalographic (EEG) measures as markers of ES.MethodsWe measured 1) amplitude, 2) power spectra, 3) entropy, 4) long-range temporal correlations, via detrended fluctuation analysis (DFA) and 5) functional connectivity of EEG data from ES patients (n=40 patients) and healthy controls (n=20 subjects), with multiple blinded measurements during wakefulness and sleep for each patient.ResultsIn ES patients, EEG amplitude was significantly higher in all electrodes. Shannon and permutation entropy were lower in ES patients than control subjects, while DFA intercept values in ES patients were significantly higher than control subjects. DFA exponent values were not significantly different between the groups. EEG functional connectivity networks in ES patients were significantly stronger than controls. Using logistic regression, a multi-attribute classifier was derived that accurately distinguished cases from controls (area under curve of 0.96).ConclusionsComputational EEG features successfully distinguish ES patients from controls in a large, blinded study.SignificanceThese objective EEG markers, in combination with other clinical factors, may speed the diagnosis and treatment of the disease, thereby improving long-term outcomes.HighlightsObjective computational EEG features may aid diagnosis of epileptic spasms (ES)ES EEG has increased delta and theta power and decreased entropy relative to controlsStronger functional connectivity networks differentiate ES patients from controls

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“…Therefore, for EEG preprocessing, most studies [16]- [18] divided EEG signals into different frequency bands. Lsu et al [19] used six finite impulse response band-pass filter in δ wave (0.5-2 Hz), sawtooth wave (2-6 Hz), θ wave (4-8 Hz), α wave (8-13 Hz), σ wave (12-14 Hz) and β wave (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), to separate the characteristic waves of EEG signals, respectively. Moreover, Memar and Faradji [17] claimed that the gamma (γ , 30-49.5 Hz) wave has a significant effect on sleep stage classification and the evidence that not using γ wave results in significant degradation in performance.…”

Section: Introductionmentioning

confidence: 99%

Automatic Sleep Stage Classification With Single Channel EEG Signal Based on Two-Layer Stacked Ensemble Model

et al. 2020

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Sleep stage classification, including wakefulness (W), rapid eye movement (REM), and nonrapid eye movement (NREM) which includes three sleep stages that describe the depth of sleep, is one of the most critical steps in effective diagnosis and treatment of sleep-related disorders. Clinically, sleep staging is performed by domain experts through visual inspection of polysomnography (PSG) recordings, which is time-consuming, labor-intensive and often subjective in nature. Therefore, this study develops an automatic sleep staging system, which uses single channel electroencephalogram (EEG) signal, for convenience of wearing and less interference in the sleep, to do automatic identification of various sleep stages. To achieve the automatic sleep staging system, this study proposes a two-layer stacked ensemble model, which combines the advantages of random forest (RF) and LightGBM (LGB), where RF focuses on reducing the variance of the proposed model while LGB focuses on reducing the bias of the proposed model. Particularly, the proposed model introduces a class balance strategy to improve the N1 stage recognition rate. In order to evaluate the performance of the proposed model, experiments are performed on two datasets, including Sleep-EDF database (SEDFDB) and Sleep-EDF Expanded database (SEDFEDB). In the SEDFDB, the overall accuracy (ACC), weight F1-score (WF1), Cohen's Kappa coefficient (Kappa), sensitivity of N1 (SEN-N1) obtained by proposed model are 91.2%, 0.916, 0.864 and 72.52% respectively using subject-non-independent test (SNT). In parallel, the ACC, WF1, Kappa, SEN-N1 obtained by proposed model are 82.4%, 0.751, 0.719 and 27.15% respectively using subject-independent test (SIT). Experimental results show that the performance of the proposed model are competitive with the state-of-the-art methods and results, and the recognition rate of N1 stage is significantly improved. Moreover, in the SEDFEDB, the experimental results indicate the robustness and generality of the proposed model. INDEX TERMS Sleep stage classification, single channel EEG signal, two-layer stacked ensemble model, random forest, LightGBM.

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Chaotic Analysis of Epileptic EEG Signals

Cited by 12 publications

References 5 publications

Long-Range Temporal Correlations Reflect Treatment Response in the Electroencephalogram of Patients with Infantile Spasms

Long-Range Temporal Correlations Reflect Treatment Response in the Electroencephalogram of Patients with Infantile Spasms

Computational characteristics of interictal EEG as objective markers of epileptic spasms

Automatic Sleep Stage Classification With Single Channel EEG Signal Based on Two-Layer Stacked Ensemble Model

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