Automatic seizure detection based on time-frequency analysis and artificial neural networks
Computational Intelligence and Neuroscience - Regular issue
Expert Systems with Applications: An International Journal
Postural time-series analysis using Empirical Mode Decomposition and second-order difference plots
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition
Research Letters in Signal Processing
Analysis of rhythms of EEG signals using orthogonal polynomial approximation
Proceedings of the 2009 International Conference on Hybrid Information Technology
Entropies for detection of epilepsy in EEG
Computer Methods and Programs in Biomedicine
Recurrent neural networks employing Lyapunov exponents for EEG signals classification
Expert Systems with Applications: An International Journal
Seizure detection in clinical EEG based on entropies and EMD
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
Computer Methods and Programs in Biomedicine
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Epilepsy is one of the most common neurological disorders characterized by transient and unexpected electrical disturbance of the brain. The electroencephalogram (EEG) is an invaluable measurement for the purpose of assessing brain activities, containing information relating to the different physiological states of the brain. It is a very effective tool for understanding the complex dynamical behavior of the brain. This paper presents the application of empirical mode decomposition (EMD) for analysis of EEG signals. The EMD decomposes a EEG signal into a finite set of bandlimited signals termed intrinsic mode functions (IMFs). The Hilbert transformation of IMFs provides analytic signal representation of IMFs. The area measured from the trace of the analytic IMFs, which have circular form in the complex plane, has been used as a feature in order to discriminate normal EEG signals from the epileptic seizure EEG signals. It has been shown that the area measure of the IMFs has given good discrimination performance. Simulation results illustrate the effectiveness of the proposed method.