Joint time and time-frequency optimal detection of K-complexes in sleep EEG
Computers and Biomedical Research
Efficient sleep spindle detection algorithm with decision tree
Expert Systems with Applications: An International Journal
On amplitude and frequency demodulation using energy operators
IEEE Transactions on Signal Processing
Teager energy and the ambiguity function
IEEE Transactions on Signal Processing
AM-FM energy detection and separation in noise using multibandenergy operators
IEEE Transactions on Signal Processing
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In this study, an efficient algorithm is proposed for the automatic detection of K-complex from EEG recordings. First, the morphology of the K-complex had been examined and the detection features were determined according to visual recognition criterions of human scorer. These features were based on amplitude and duration properties of K-complex waveform. The algorithm is based on wavelet and teager energy operator and includes two main stages. Both results of stages were combined to make robust decision. The EEG recordings obtained from the Sleep Research Laboratory in Department of Psychiatry at Gulhane Military Medical Academy. All night sleep EEG data, total 1045 epochs and 690 of these are NREM 2 stage, from 25 years old healthy female subject were used. Three scorers inspected recording separately to score K-complexes. The detection algorithm was then tested on the same recording. The results show that the agreements between the scorers were fairly different. The results are evaluated with the ROC analysis which proves up to 91% success in detecting the K-complex.