Neural network for sleep EEG K-complex detection

The paper presents the development and application of an automatic system used to detect and classify the K-complexes aperiodic, waveforms found in electroencephalograms of patients during stage two sleep. The slow-wave transient K-complex is evoked by auditory or somatosensory stimulation being an event related potential. The analysis of this transitory waveform contributes to the assessment of sleep stages used by controlled learning during sleep. In our work we used a TMS320C30 DSP to implement an automatic detection procedure based on features extraction and classification using a feed-forward neural network.