Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised classification and adaptive definition of sleep patterns
Pattern Recognition Letters
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Extended ICA removes artifacts from electroencephalographic recordings
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
ECG De-noising Based on Empirical Mode Decomposition
ICYCS '08 Proceedings of the 2008 The 9th International Conference for Young Computer Scientists
Expert Systems with Applications: An International Journal
Neuro-fuzzy closed-loop control of depth of anaesthesia
Artificial Intelligence in Medicine
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In this paper, the work is mainly concentrated on removing non-linear parameters to make the physiological signals more linear and reducing the complexity of the signals. This paper discusses three different types of techniques that can be successfully utilised to remove non-linear parameters in EEG and ECG. i Transformation technique using Discrete Walsh-Hadamard Transform DWHT; ii application of fuzzy logic control and iii building the Adaptive Neuro-Fuzzy Inference System ANFIS model for fuzzy. This work has been inspired by the need to arrive at an efficient, simple, accurate and quicker method for analysis of bio-signal.