Statistical Analysis Of Brain Signals For Epileptology PDF Download

"Background: Epilepsy is one of the most common neurological disorders in the world, affecting approximately 1% of the world's population. It can cause brain activity to become abnormal causing seizures. There are various medical risks associated with seizure onset, as well as a decrease in quality of life for patients diagnosed with epilepsy. However, when it comes to studying physiological signals in patients with epilepsy most of data used to study epileptic seizures are in the short-term surrounding the ictal period (the seizure itself). This assumes that the inter-ictal period is stable, which is at odds with the already established, long-term biological rhythms that are present in physiological signals. So, by studying long-term physiological signals in the context of epilepsy, a better sense of how the complex bodily rhythms present affect physiological signals at time of seizure onset.Objectives: The main objective this project involved the analysis of long-term analysis of physiological signals from subjects with epilepsy, as well as the determination of the periodic components on those signals and their correlation with seizure onset.Methods: This project was divided in two parts with different datasets used for each one. In Part I, an HRV signal was built from the detected R peaks of the ECG signals. The HRV was then fed into an MVAR model, and the power spectral density matrices were computed. The HF and LF components of the HRV were isolated and their periodicities were estimated. Circular statistics were then used to calculate the correlation of the periodic signal to seizure onset. In Part II, the mean intracranial EEG signal was computed, the five frequency bands were extracted, and their envelope is computed. Then the HRV and EEG-ENV were fed into the MVAR model, then power spectral density matrices and coherence were computed. The periodicities were estimated, and finally circular statistics was used to compute the correlation to seizure onset in group level and in a subject specific manner.Results: For Part I, the main periodic components seen in the HRV, HF and LF signals were at approximately four, twelve and twenty-four hours. Correlation with seizure onset were seen in the HF signal at the twelve-hour periodicity and in the LF signal at the twenty-four periodicity. For Part II, the time-varying coherence of the theta, alpha, and gamma bands with the HRV-LF were more coupled, however the delta and beta bands were the most distinct ones. The periodicities detected in the EEG envelopes were distributed in a more spread-out way than the HRV-LF. The time-varying coherence had less periodic components then the EEG-ENV and HRV-LF signals. Correlation with seizure onset had a wide assortment of variation across subjects and frequency bands. At a group level, the strongest correlations detected were for the circadian periodicities of the HRV-LF and the Gamma-ENV"--