Detection of epileptic seizure activities from long-term multi-channel electroencephalogram (EEG) signals plays a significant role in the timely treatment of the patients with epilepsy. Visual identification of epileptic seizure in long-term EEG is cumbersome and tedious for neurologists, which might also lead to human error. Therefore, an automated tool for accurate detection of seizures in a long-term multi-channel EEG is essential for the clinical diagnosis. This study proposes an algorithm using multi-features and multilayer perceptron neural network (MLPNN) classifier. After appropriate approval from the ethical committee, recordings of EEG data were collected from the Institute of Neurosciences, Ramaiah Memorial College and Hospital, Bengaluru. Initially, preprocessing was performed to remove the power-line noise and motion artifacts. Four features, namely power spectral density (Yule–Walker), entropy (Shannon and Renyi), and Teager energy, were extracted. The Wilcoxon rank-sum test and descriptive analysis ensure the suitability of the proposed features for pattern classification. Single and multi-features were fed to the MLPNN classifier to evaluate the performance of the study. The simulation results showed sensitivity, specificity, and false detection rate of 97.1%, 97.8%, and 1 h−1, respectively, using multi-features. Further, the results indicate the proposed study is suitable for real-time seizure recognition from multi-channel EEG recording. The graphical user interface was developed in MATLAB to provide an automated biomarker for normal and epileptic EEG signals.
A colored-noise Kalman filter is designed to diminish the error effects caused by sensors placed on vibrating structures. This paper deals with sensors that are used to estimate position, attitude or both. Here we focus on a vision-based system, which uses a set of lightemitting diode beacons with a focal plane detector to determine line-of-sight measurements. Estimation of both position and attitude is possible with this system. Vibrational effects are added to the beacon locations and a colored-noise filter is designed to mitigate the effects of the beacon movements on state estimation. A sensitivity study is conducted for this paper work, where the effects of beacon location errors on the estimation of a vehicle's position and attitude are examined. Beacon location variation is introduced into the standard vision-based navigation problem as second-order vibration noise. Further, an error in the process-noise covariance is assumed and its effect on the estimated quantity is observed. Different magnitudes of vibration are added to the beacons position and the robustness properties of the colored-noise filter is analyzed. Results indicate that the colored-noise filter provides significant improvements over a filter that does not account for vibrational effects.
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