A wavelet and teager energy operator based method for automatic detection of K-Complex in sleep EEG

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.

[1]  J. R. Smith,et al.  Automatic detection of the K-complex in sleep electroencephalograms. , 1970, IEEE transactions on bio-medical engineering.

[2]  Petros Maragos,et al.  AM-FM energy detection and separation in noise using multiband energy operators , 1993, IEEE Trans. Signal Process..

[3]  Jaakko Astola,et al.  Teager energy and the ambiguity function , 1999, IEEE Trans. Signal Process..

[4]  Osman Erogul,et al.  Efficient sleep spindle detection algorithm with decision tree , 2009, Expert Syst. Appl..

[5]  Gerhard Klösch,et al.  Scalp Topography of the Spontaneous K-Complex and of Delta-Waves in Human Sleep , 2004, Brain Topography.

[6]  Laboratoire Lm.  Joint Time and Time-Frequency Optimal Detection of K-Complexes in Sleep EEG , 1998 .

[7]  J. Shaw,et al.  The form voltage distribution and physiological significance of the K-complex. , 1956, Electroencephalography and clinical neurophysiology.

[8]  Naohiro Ishii,et al.  Detection of the K-Complex Using a New Method of Recognizing Waveform Based on the Discrete Wavelet Transform , 1995, IEICE Trans. Inf. Syst..

[9]  E. Wolpert.  A Manual of Standardized Terminology, Techniques and Scoring System for Sleep Stages of Human Subjects. , 1969 .

[10]  M Steriade,et al.  Slow sleep oscillation, rhythmic K‐complexes, and their paroxysmal developments , 1998, Journal of sleep research.

[11]  Petros Maragos,et al.  On amplitude and frequency demodulation using energy operators , 1993, IEEE Trans. Signal Process..

[12]  B.H. Jansen.  Artificial neural nets for K-complex detection , 1990, IEEE Engineering in Medicine and Biology Magazine.

[13]  H. Berger.  Über das Elektrenkephalogramm des Menschen , 1933, Archiv für Psychiatrie und Nervenkrankheiten.

[14]  F. H. Lopes da Silva,et al.  A model-based detector of vertex waves and K complexes in sleep electroencephalogram. , 1991, Electroencephalography and clinical neurophysiology.

[15]  H. Teager.  Some observations on oral air flow during phonation , 1980 .

[16]  I. Bankman,et al.  Feature-based detection of the K-complex wave in the human electroencephalogram using neural networks , 1992, IEEE Transactions on Biomedical Engineering.

[17]  Alfred L. Loomis,et al.  DISTRIBUTION OF DISTURBANCE-PATTERNS IN THE HUMAN ELECTROENCEPHALOGRAM, WITH SPECIAL REFERENCE TO SLEEP , 1938 .

[18]  Peter Braude,et al.  Atlas of Sleep Medicine , 1991 .

[19]  L. Johnson,et al.  Autonomic correlates of the spontaneous K-complex. , 1968, Psychophysiology.

[20]  Ben H. Jansen,et al.  K-complex detection using multi-layer recurrent networks perceptrons and , 1994 .