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This paper presents an automated method for seizure detection in EEGs using an increment entropy (IncrEn) and support vector machines (SVMs). The IncrEn is a measure of the complexity of time series, which characterizes both the permutation of values and the temporal order of values. The IncrEn is used to extract features of epileptic EEGs and normal EEGs. The SVMs are employed to classify seizure...
In this paper we describe a simple and very fast method of data acquisition, feature extraction and feature space creation for epileptic seizure detection. The scalp electroencephalogram (EEG) dataset [1, 2] collected at the Children's Hospital Boston from 22 pediatric patients having 192 intractable seizures (available as CHB-MIT database) is used to assess this simple approach against existing ones...
This paper presents a novel patient-specific algorithm for detection of seizures in epileptic patients from a single-channel intra-cranial electroencephaolograph (iEEG) recording. Instead of extracting features from the EEG signal, first the EEG signal is filtered by a prediction error filter (PEF) to compute a prediction error signal. A two-level wavelet decomposition of the prediction error signal...
Routine electroencephalogram (EEG) is an important test in aiding the diagnosis of patients with suspected epilepsy. These recordings typically last 20-40 minutes, during which signs of abnormal activity (spikes, sharp waves) are looked for in the EEG trace. It is essential that events of short duration are detected during the routine EEG test. The work presented in this paper examines the effect...
Two systems based on different classifiers are compared for the task of neonatal seizure detection. Support vector machines and Gaussian mixture models are presented as examples of discriminative and generative approaches to classification. The performance of both systems is assessed using a number of metrics, the results of which indicate that both systems are competitive with other detectors in...
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