A strategy combining intrinsic time-scale decomposition and a feedforward neural network for automatic seizure detection
Epilepsy is a common neurological disorder which can occur in people of all ages globally. For the
clinical treatment of epileptic patients, the detection of epileptic seizures is of great
significance. Objective : Electroencephalography (EEG) is an essential component in the diagnosis of
epileptic seizures, from which brain surgeons can detect important pathological information about
patient epileptiform discharges. This paper focuses on adaptive seizure detection from EEG
recordings. We propose a new feature extraction model based on an adaptive decomposition method,
named intrinsic time-scale decomposition (ITD), which is suitable for analyzing non-linear and
non-stationary data. Approach : Firstly, using the ITD technique, every EEG recording is decomposed
into several proper rotation components (PRCs). Secondly, the instantaneous amplitudes and
frequencies of these PRCs can be calculated and then we extract their statistical indices.
Furthermore, we combine all ...
Source: Physiological Measurement - Category: Physiology Authors: Lijun Yang, Sijia Ding, Hao-min Zhou and Xiaohui Yang Source Type: research