1. Acharya, U.R., Sree, S.V., Swapna, G., Martis, R.J., Suri, J.S. (2013). Automated EEG analysis of epilepsy: A review. Knowledge-based Systems, 45, 147–165.
https://doi.org/10.1016/j.knosys.2013.02.014
.
http://www.sciencedirect.com/science/article/pii/S0950705113000798
.
2. Alarcon-Aquino, V., & Barria, J. (2009). Change detection in time series using the maximal overlap discrete wavelet transforms. Latin American Applied Research: An International Journal, 39(2), 145–152.
3. Alarcon-Aquino, V., Ramírez-Cortés, J., Gómez-Gil, P., Starostenko, O., García-González, Y. (2014). Network intrusion detection using self-recurrent wavelet-neural-network with multidimensional radial wavelon. Information Technology and Control, 43(4), 347–358.
4. Andrzejak, R., Lehnertz, K. , Rieke, C., Mormann, F., David, P., Elger, C. (2001). Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Phy. Rev, 64(6), 061907–1–061907-8.
5. Anusha, K.S., Mathew, T.M., Subha, D.P. (2012). Classification of normal and epileptic EEG signal using time & frequency domain features through artificial neural network. In: 2012 international conference on advances in computing and communications (pp. 98–101).