Performance Analysis of ICA with GMM and HMM for Epilepsy Classification

Author:

Rajaguru Harikumar,Prabhakar Sunil Kumar

Publisher

Springer International Publishing

Reference18 articles.

1. Prabhakar, S.K., Rajaguru, H.: Code converters with city block distance measures for classifying epilepsy from EEG signals. In: Procedia Comput. Sci. 87, 5–11 (2016). Fourth International Conference on Recent Trends in Computer Science & Engineering, Chennai, India, 29–30 April 2016

2. Prabhakar, S.K., Rajaguru, H.: Expectation maximization based PCA and Hessian LLE with suitable post classifiers for epilepsy classification from EEG signals. In: Proceedings of the 8th International Conference on Soft Computing and Pattern Recognition (SoCPaR), VIT University, Vellore, India, December 2016 (2016)

3. Prabhakar, S.K., Rajaguru, H.: Performance analysis of ApEn as a feature extraction technique and time delay neural networks, multi layer perceptron as post classifiers for the classification of epilepsy risk levels from EEG signals. In: Proceedings of ICC3 2015, Computational Intelligence, Cyber Security and Computational Models. Advances in Intelligent Systems and Computing, Series, PSG College of Technology, Coimbatore, India, 17–19 December 2015, vol. 412, pp. 89–97. Springer (2015)

4. Prabhakar, S.K., Rajaguru, H.: Particle swarm based sparse representation classifier for classification of epilepsy from EEG signals. In: 14th Electrical Engineering/Electronics, Computer, Telecommunications, and Information Technology Conference (ECTICON 2017) held at Phuket, Thailand, 27–30 June, 2017 (2017)

5. Prabhakar, S.K., Rajaguru, H.: Conceptual analysis of epilepsy classification using probabilistic mixture models. In: 5th IEEE Winter International Conference on Brain-Computer Interface, South Korea, 9–11 January, 2017 (2017)

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