Abstract:
Worldwide survey from health department indicates that approximately 50 million people are
currently affected with epilepsy, which is caused due to seizure. Among the top four common
neurological diseases in the United States after migraine, stoke and Alzheimer‟s disease is
epilepsy. Internationally, a vague count of average epilepsy patient‟s each year is 2.4 million.
Electroencephalogram (EEG) monitors the electrical activity inside our brain, which is due to the
movement of neurons. It is used for the in time detection of various diseases in neonatal and
adults, such as a seizure. EEG displays the signals received by our brain from all body parts.
Any sort of seizure that is likely to occur in our body or brain can be seen through EEG. As only
time or frequency analysis is not sufficient to clearly depict the non-stationery electrical activity.
Time-frequency (TF) analysis is helpful for the dynamic property of EEG signals. The signal is
affected by different artefacts, which produce false detections.
Distinct research has been carried out in this field. Various methods have been tested for
extracting features of the EEG signal; also classifiers, such as Neural Networks and support
vector machine (SVM), were applied for the detection purpose. TF representation provides a
wealth of information about the underlying EEG in temporal as well as spectral domains. This
work will use novel image-processing methods and machine learning procedures for the feature
extraction stages to improve the accuracy (in terms of both sensitivity and specificity) of existing
methods. The understanding and assessment about epilepsy is still a long way ahead. Epilepsy
awareness and its care among the masses are below a considerate level. This work will assist the
doctors in the field of neurology to improve the timely detection of seizures.
