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Epilepsy is a neurological disorder that affects around 50 million people worldwide. The seizure detection is an important component in the diagnosis of epilepsy. In this study, the Empirical Mode Decomposition (EMD) method was proposed on the development of an automatic epileptic seizure detection algorithm. The algorithm first computes the Intrinsic Mode Functions (IMFs) of EEG records, then calculates...
A system of epilepsy seizure detection in real life conditions and based on inertial sensors is presented in this paper with a focus on the signal processing to recognize seizure moves. This system is based on several models of signals, one corresponding to general movements, and two others describing seizures moves. The detection algorithm evaluates for a given time window which model fits the best...
This paper describes a new approach in features extraction using time-frequency distributions (TFDs) for detecting epileptic seizures to identify abnormalities in electroencephalogram (EEG). Particularly, the method extracts features using the smoothed pseudo Wigner-Ville distribution combined with the McAulay-Quatieri sinusoidal model and identifies abnormal neural discharges. We propose a new feature...
Epilepsy is a neurological disorder which causes two million people in the United States for suffering. In this research, we proposed a seizure detection method based on intracranial electroencephalogram (IEEG) by using signal processing and intelligence computing approach, which combines filtering, back propagation neural network (BPNN), multi-resolution Teager energy operator (MTEO), smooth window,...
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