5. Conclusions
It was shown that recurrent neural networks with relatively simple architectures are capable of detecting seizure-like activity in multi-channel EEG records. Furthermore, the results obtained from the three different architectures studied were fairly consistent in locating the generators of seizure-like activity based on the patient’s subdural EEG montage. If these results are verified by other studies, then these neural networks may serve as effective functional engines in more sophisticated seizure detection utilities. Additional work to increase the detection ability of these networks needs to be undertaken, especially with regard to the separation of the EEG data recorded at each electrode. Also, a means by which a utility using these neural networks can be made more adaptive to a variety of seizures from a wide patient population needs to be examined.
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Bates, R.R., Sun, M., Scheuer, M.L., Sclabassi, R.J. (2005). Analysis of Multi-Channel Subdural EEG by Recurrent Neural Networks. In: Attoh-Okine, N.O., Ayyub, B.M. (eds) Applied Research in Uncertainty Modeling and Analysis. International Series in Intelligent Technologies, vol 20. Springer, Boston, MA. https://doi.org/10.1007/0-387-23550-7_7
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