Variability analysis of epileptic EEG using the maximal overlap discrete wavelet transform
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Variability analysis of epileptic EEG using the maximal overlap discrete wavelet transform Jack L. Follis1 and Dejian Lai2*
Abstract Purpose: To determine if there is a difference in the wavelet variances of seizure and non-seizure channels in the EEG of an epileptic subject. Methods: A six-level decomposition was applied using the Maximal Overlap Discrete Wavelet Transform (MODWT). The wavelet variance and 95% CIs were calculated for each level of the decomposition. The number of changes in variance for each level were found using a change-point detection method of Whitcher. The Kruskal–Wallis test was used to determine if there were differences in the median number of change points within channels and across frequency bands (levels). Results: No distinctive pattern was found for the wavelet variances to differentiate the seizure and non-seizure channels. The seizure channels tended to have lower variances for each level and overall, but this pattern only held for one of the three seizure channels (RAST4). The median number of change points did not differ between the seizure and non-seizure channels either within each channel or across the frequency bands. Conclusion: The use of the MODWT in examining the variances and changes in variance did not show specific patterns which differentiate between seizure and non-seizure channels. Keywords: EEG, Epilepsy, Kruskal–Wallis test, Wavelet transformation, Whitcher test Introduction One of main tools involved in epilepsy research and diagnosis is the electroencephalogram (EEG) [1]. The inspection of these signals is mainly done visually by examining features such as frequency, voltage and waveform activity along with persistence of any abnormalities [2, 3]. However, the visual examination is limited by potential subjectivity due to limited protocols and the inability to identify hidden patterns, characteristics or relationships in large amounts of data [4, 3]. The introduction of quantitative methods in the analysis of EEGs attempt to overcome these limitations by introducing objective measures of EEG characteristics [5, 6]. These methods give both the clinician and/or researcher access to the valuable information within the EEG concerning the dynamics of *Correspondence: [email protected] 2 Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, School of Public Health, 1200 Herman Pressler Drive, W‑1008, Houston, TX 77030, USA Full list of author information is available at the end of the article © Springer Nature Switzerland AG 2020.
brain activity. However, the EEG, like many physiological time series, is widely believed to be non-stationary due to its time-varying properties; the non-stationarity may arise from the fact that the observation time is shorter than the characteristic time scale of the EEG [7]. Due to the non-stationarity of the EEG, many classic time series approaches based on stationarity may not be appropriate in their analysis. Much of the quant
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