6,014 research outputs found
Comparison of EEG based epilepsy diagnosis using neural networks and wavelet transform
Epilepsy is one of the common neurological disorders characterized by
recurrent and uncontrollable seizures, which seriously affect the life of
patients. In many cases, electroencephalograms signal can provide important
physiological information about the activity of the human brain which can be
used to diagnose epilepsy. However, visual inspection of a large number of
electroencephalogram signals is very time-consuming and can often lead to
inconsistencies in physicians' diagnoses. Quantification of abnormalities in
brain signals can indicate brain conditions and pathology so the
electroencephalogram (EEG) signal plays a key role in the diagnosis of
epilepsy. In this article, an attempt has been made to create a single
instruction for diagnosing epilepsy, which consists of two steps. In the first
step, a low-pass filter was used to preprocess the data and three separate
mid-pass filters for different frequency bands and a multilayer neural network
were designed. In the second step, the wavelet transform technique was used to
process data. In particular, this paper proposes a multilayer perceptron neural
network classifier for the diagnosis of epilepsy, that requires normal data and
epilepsy data for education, but this classifier can recognize normal
disorders, epilepsy, and even other disorders taught in educational examples.
Also, the value of using electroencephalogram signal has been evaluated in two
ways: using wavelet transform and non-using wavelet transform. Finally, the
evaluation results indicate a relatively uniform impact factor on the use or
non-use of wavelet transform on the improvement of epilepsy data functions, but
in the end, it was shown that the use of perceptron multilayer neural network
can provide a higher accuracy coefficient for experts.Comment: 8 pages, 4 tables, 3 figure
The status of textile-based dry EEG electrodes
Electroencephalogram (EEG) is the biopotential recording of electrical signals generated by brain activity. It is useful for monitoring sleep quality and alertness, clinical applications, diagnosis, and treatment of patients with epilepsy, disease of Parkinson and other neurological disorders, as well as continuous monitoring of tiredness/ alertness in the field. We provide a review of textile-based EEG. Most of the developed textile-based EEGs remain on shelves only as published research results due to a limitation of flexibility, stickability, and washability, although the respective authors of the works reported that signals were obtained comparable to standard EEG. In addition, nearly all published works were not quantitatively compared and contrasted with conventional wet electrodes to prove feasibility for the actual application. This scenario would probably continue to give a publication credit, but does not add to the growth of the specific field, unless otherwise new integration approaches and new conductive polymer composites are evolved to make the application of textile-based EEG happen for bio-potential monitoring
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