Sleep EEG Event Detection Using Fuzzy_Neural Approach

The wide variety of waveforms in EEG signals and the high non-stationary nature of many of them is one of the main difficulties to develop automatic detection systems for them. In sleep stage classification a relevant transient wave is the K-complex. This report comprehends the developing of new Fuzzy_Neural algorithm in order to achieve an automatic K-complex detection from EEG raw data. The Fuzzy c-means algorithm is used for the rough and rapid recognition of K-complex and the Neural Network classifier does the exact evaluation on the detected K-complex. This Pattern recognition technique is a hardware independent solution for the biomedical signal processing field. This represents a significant criterion for the objective assessment of a patient’s sleep quality.