Joint time and time-frequency optimal detection of K-complexes in sleep EEG

Automated detection of waveforms such as delta and K-complex in the EEG is an important component of sleep stage monitoring. The K-complex is a key feature that contributes to sleep stages assessment. However, its automated detection is still difficult due to the stochastic nature of the EEG. In this paper, we propose a detection structure which can be interpreted as joint linear filtering operations in time and time-frequency domains. We also introduce a method of obtaining the optimum detector from training data, and we show that the resulting receiver offers better performances than the one obtained via the Fisher criterion maximization. The efficiency of this approach for K-complexes detector design is explored. It results from this study that the obtained receiver is potentially the best one which can be found in the literature. Finally, it is emphasized that this methodology can be advantageously used to solve many other detection problems.