Automated detection of waveforms such as delta and K-complexes 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 the 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 advantageously be used to solve many other detection problems. 3
Another study was presented by REF , in which the k-complexes were recognized based on a joint linear filter in time and time-frequency domains.
JOINT TIME AND TIME- ‐ FREQUENCY OPTIMAL DETECTION OF K-‐COMPLEXES IN SLEEP EEG
Computers and biomedical research, an international journal
Computers and biomedical research, an international journal
Computer Science Medicine