A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A Mathematical Theory of Communication
A Mathematical Theory of Communication
Genetic programming for simultaneous feature selection and classifier design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On the Complexity of Finite Sequences
IEEE Transactions on Information Theory
Expert Systems with Applications: An International Journal
Comparative study of approximate entropy and sample entropy robustness to spikes
Artificial Intelligence in Medicine
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Engineering Applications of Artificial Intelligence
Seizure detection in clinical EEG based on entropies and EMD
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
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Objective: In this paper, electroencephalogram (EEG) signals of 20 schizophrenic patients and 20 age-matched control participants are analyzed with the objective of classifying the two groups. Materials and methods: For each case, 20 channels of EEG are recorded. Several features including Shannon entropy, spectral entropy, approximate entropy, Lempel-Ziv complexity and Higuchi fractal dimension are extracted from EEG signals. Leave-one (participant)-out cross-validation is used for reliable estimate of the separability of the two groups. The training set is used for training the two classifiers, namely, linear discriminant analysis (LDA) and adaptive boosting (Adaboost). Each classifier is assessed using the test dataset. Results: A classification accuracy of 86% and 90% is obtained by LDA and Adaboost respectively. For further improvement, genetic programming is employed to select the best features and remove the redundant ones. Applying the two classifiers to the reduced feature set, a classification accuracy of 89% and 91% is obtained by LDA and Adaboost respectively. The proposed technique is compared and contrasted with a recently reported method and it is demonstrated that a considerably enhanced performance is achieved. Conclusion: This study shows that EEG signals can be a useful tool for discrimination of the schizophrenic and control participants. It is suggested that this analysis can be a complementary tool to help psychiatrists diagnosing schizophrenic patients.