Fuzzy Modeling for Control
Nonlinear Biomedical Signal Processing: Fuzzy Logic, Neural Networks, and New Algorithms
Nonlinear Biomedical Signal Processing: Fuzzy Logic, Neural Networks, and New Algorithms
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A study on fuzzy C-means clustering-based systems in automatic spike detection
Computers in Biology and Medicine
A new unsupervised approach for fuzzy clustering
Fuzzy Sets and Systems
A hybridized approach to data clustering
Expert Systems with Applications: An International Journal
A new approach for epileptic seizure detection using adaptive neural network
Expert Systems with Applications: An International Journal
An efficient classifier to diagnose of schizophrenia based on the EEG signals
Expert Systems with Applications: An International Journal
Robustness of density-based clustering methods with various neighborhood relations
Fuzzy Sets and Systems
Clustering biological data using voronoi diagram
ADCONS'11 Proceedings of the 2011 international conference on Advanced Computing, Networking and Security
Fuzzy and crisp clustering methods based on the neighborhood concept: A comprehensive review
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - FUZZYSS'2011: 2nd International Fuzzy Systems Symposium
| Hi-index | 12.05 |
Bispectral index scale (BIS) is a continuous processed electroencephalogram (EEG) parameter that correlates to the patient's level of brain activity, where 100 is awake and 0 (flat line) is dead. BIS was designed to correlate with ''hypnotic'' clinical endpoints (sedation, lack of awareness, and memory) and to track changes in the effects of anesthetics on the brain. In this study, an approach to utilize clustering methods is investigated in the analysis of BIS series data. Fuzzy c-Means (The FCM) and Fuzzy Neighborhood DBSCAN (FN-DBSCAN) algorithms are handled in the paper. The FN-DBSCAN algorithm is advantageous in such a way that it aggregates the speed of the well-known Density Based Spatial Clustering of Applications with Noise (DBSCAN) and the robustness of the Noise-Robust Fuzzy Joint Points (NRFJP) algorithms. As a result of the computational experiments, we can conclude that FN-DBSCAN method gives more realistic results to recognize the stable duration intervals and the BIS stages in the measurement series.