Diagnosis of Epileptic Seizure from EEG Signals by Least Squares Method

Epilepsy is characterized by temporary and unexpected electrical deterioration in brain. EEG is preferred in diagnosis. There are many studies in the literature on EEG signals to differentiate between groups in epileptic and non-epileptic individuals. In this study, EEG signals were examined for predicting seizures for the three pre-, during, and post-seizures. The EEG was filtered by Singular Spectrum Analysis. Then the maximum amplitude wave in the EEG signal is fitted to the exponential curve by the nonlinear Least Squares method. The slope of the exponential curve is obtained as a feature. The obtained feature was examined statistically. As a result, there was a significant difference between the during seizure and post seizure, pre-seziure and post-seizure. There was no difference between pre-seizure and during seizure

Quantitative Electroencephalography Analysis for Improved Assessment of Consciousness Levels in Deep Coma Patients Using a Proposed Stimulus Stage

“Coma” is defined as an inability to obey commands, to speak, or to open the eyes. So, a coma is a state of unarousable unconsciousness. In a clinical setting, the ability to respond to a command is often used to infer consciousness. Evaluation of the patient’s level of consciousness (LeOC) is important for neurological evaluation. The Glasgow Coma Scale (GCS) is the most widely used and popular scoring system for neurological evaluation and is used to assess a patient’s level of consciousness. The aim of this study is the evaluation of GCSs with an objective approach based on numerical results. So, EEG signals were recorded from 39 patients in a coma state with a new procedure proposed by us in a deep coma state (GCS: between 3 and 8). The EEG signals were divided into four sub-bands as alpha, beta, delta, and theta, and their power spectral density was calculated. As a result of power spectral analysis, 10 different features were extracted from EEG signals in the time and frequency domains. The features were statistically analyzed to differentiate the different LeOC and to relate with the GCS. Additionally, some machine learning algorithms have been used to measure the performance of the features for distinguishing patients with different GCSs in a deep coma. This study demonstrated that GCS 3 and GCS 8 patients were classified from other levels of consciousness in terms of decreased theta activity. To the best of our knowledge, this is the first study to classify patients in a deep coma (GCS between 3 and 8) with 96.44% classification performance