713 research outputs found

    Seizure characterisation using frequency-dependent multivariate dynamics

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    The characterisation of epileptic seizures assists in the design of targeted pharmaceutical seizure prevention techniques and pre-surgical evaluations. In this paper, we expand on recent use of multivariate techniques to study the crosscorrelation dynamics between electroencephalographic (EEG) channels. The Maximum Overlap Discrete Wavelet Transform (MODWT) is applied in order to separate the EEG channels into their underlying frequencies. The dynamics of the cross-correlation matrix between channels, at each frequency, are then analysed in terms of the eigenspectrum. By examination of the eigenspectrum, we show that it is possible to identify frequency dependent changes in the correlation structure between channels which may be indicative of seizure activity. The technique is applied to EEG epileptiform data and the results indicate that the correlation dynamics vary over time and frequency, with larger correlations between channels at high frequencies. Additionally, a redistribution of wavelet energy is found, with increased fractional energy demonstrating the relative importance of high frequencies during seizures. Dynamical changes also occur in both correlation and energy at lower frequencies during seizures, suggesting that monitoring frequency dependent correlation structure can characterise changes in EEG signals during these. Future work will involve the study of other large eigenvalues and inter-frequency correlations to determine additional seizure characteristics

    Seizure Detection with Artificial Neural Networks

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    Multiscaled Cross-Correlation Dynamics in Financial Time-Series

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    The cross correlation matrix between equities comprises multiple interactions between traders with varying strategies and time horizons. In this paper, we use the Maximum Overlap Discrete Wavelet Transform to calculate correlation matrices over different timescales and then explore the eigenvalue spectrum over sliding time windows. The dynamics of the eigenvalue spectrum at different times and scales provides insight into the interactions between the numerous constituents involved. Eigenvalue dynamics are examined for both medium and high-frequency equity returns, with the associated correlation structure shown to be dependent on both time and scale. Additionally, the Epps effect is established using this multivariate method and analyzed at longer scales than previously studied. A partition of the eigenvalue time-series demonstrates, at very short scales, the emergence of negative returns when the largest eigenvalue is greatest. Finally, a portfolio optimization shows the importance of timescale information in the context of risk management

    Analysis of Epileptic Seizure Using Wavelet Transform

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    In this work, wavelet transform (WT) is used to analyze epileptic seizure in recorded EEG signals. Wavelets allow non-stationary EEG signals to be decomposed into elementary forms at different positions and scales. The extracted features from the WT decomposition is then expressed in terms wavelet based and classical based features to be further analyzed. In general, the coefficients of a 1-D wavelet decomposition comprises of approximate and detail coefficient, arranged in a single row. The number of wavelet coefficients depends on the decomposition level with more coefficients at high decomposition level. The features generated from wavelet transform is tested in terms of discriminatory information and the highly informative features will be identified. To select the best features, Fisher Discriminant Ratio (FDR) is implemented and classification error was calculated using Support Vector Machine (SVM). When FDR is applied, amongst all the 23 channels, certain channels will be dominant over the other channels in terms of value and these channels are then be chosen for the reduced feature analysis
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