A practical handbook of speech coders
A practical handbook of speech coders
Selection of optimal AR spectral estimation method for EEG signals using Cramer-Rao bound
Computers in Biology and Medicine
Comparison of subspace-based methods with AR parametric methods in epileptic seizure detection
Computers in Biology and Medicine
Recurrent neural networks employing Lyapunov exponents for EEG signals classification
Expert Systems with Applications: An International Journal
Automated system for detection of epileptiform patterns in EEG by using a modified RBFN classifier
Expert Systems with Applications: An International Journal
Automated EEG analysis of epilepsy: A review
Knowledge-Based Systems
Computer Methods and Programs in Biomedicine
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In this study, a method is proposed to detect epileptic seizures over EEG signal. For this purpose, a linear prediction filter is used to observe the presence of spikes and sharp waves on seizure EEG recordings. Linear prediction analysis calculates a coefficient set for each window, which can best model the applied time series signal. Modeling success is observed on the prediction error signal. The presence of spikes and other seizure-specific sharp waves on the signal reduces the modeling success and increases the prediction error of the filter. It is clearly observed that, the energy of prediction error signal during seizures is much higher than that of the seizure free intervals, which indicates the energy value and can be used to locate the seizure interval. The method is applied to 250 distinct EEG records, each of which has 23.6s duration. The results of the proposed algorithm are evaluated with the ROC analysis which indicates 93.6% success in detecting the presence of seizures. As a conclusion, the linear prediction error energy method can be considered as an efficient way to detect epileptic seizures on EEG records.