K-Complex Detection Based on Synchrosqueezing Transform

Document Type : Research Article


Faculty of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran


K-complex is an underlying pattern in the sleep EEG. Due to the role of sleep studies in
neurophysiologic and cognitive disorders diagnosis, reliable methods for analysis and detection of this pattern
are of great importance. In our previous work, Synchrosqueezing Transform (SST) was proposed for analysis
of this pattern. SST is an EMD-like tool, which benefits from wavelet transform and reallocation approaches.
This method is able to decompose signals into their time-varying oscillatory ingredients. In addition, it
provides a time-frequency representation with less blurring compared to wavelet transform. In this paper,
firstly, the ability of SST is investigated by applying the ANOVA test, which is approved by proper p-values.
This paper proposes SST for K-complex detection. The proposed method is based on a so-called “detection
of K-complexes and sleep spindles” (DETOKS) framework. DETOKS is based on spares optimization
and decomposes signals into four components, namely transient, low frequency, oscillatory, and a residual.
Applying the Teager-Kaiser energy operator and setting a threshold on the low-frequency component result
in K-complex detection. We modify DETOKS using SST. The proposed method is applied to DREAMS
dataset. The dataset provides two visual scorings accompanied by an automatic one. As the visual labels were
extremely different, the automatic detection is considered as the third expert’s scoring and data is re-labeled
by a voting approach among three experts. For DETOKS, DETOKS modified by CWT, and the proposed
method, MCC measure is 0.62, 0.71, and 0.76, respectively. It shows superiority of the proposed method.


Main Subjects

[1] E. Hernández-Pereira, V. Bolón-Canedo, N. Sánchez- Maroño, D. Álvarez-Estévez, V. Moret-Bonillo, A. Alonso-Betanzos, A comparison of performance of K-complex classification methods using feature selection, Information Sciences, 328 (2016) 1-14.
[2] T. Lajnef, S. Chaibi, J.-B. Eichenlaub, P.M. Ruby, P.-E. Aguera, M. Samet, A. Kachouri, K. Jerbi, Sleep spindle and K-complex detection using tunable Q-factor wavelet transform and morphological component analysis, Frontiers in human neuroscience, 9 (2015).
[3] A.L. Pinto, I.S. Fernández, J.M. Peters, S. Manganaro, J.M. Singer, M. Vendrame, S.P. Prabhu, T. Loddenkemper, S.V. Kothare, Localization of sleep spindles, k-complexes, and vertex waves with subdural electrodes in children, Journal of Clinical Neurophysiology, 31(4) (2014) 367-374.
[4] V. Kokkinos, G.K. Kostopoulos, Human non.rapid eye movement stage II sleep spindles are blocked upon spontaneous K.complex coincidence and resume as higher frequency spindles afterwards, Journal of sleep research, 20(1pt1) (2011) 57-72.
[5] http://www.tcts.fpms.ac.be/~devuyst/Databases/ DatabaseKcomplexes/
[6] V. Kokkinos, A.M. Koupparis, G.K. Kostopoulos, An intra-K-complex oscillation with independent and labile frequency and topography in NREM sleep, Frontiers in human neuroscience, 7 (2013).
[7] W.O. Tatum IV, Handbook of EEG interpretation, Demos Medical Publishing, 2014.
[8] T.A. Camilleri, K.P. Camilleri, S.G. Fabri, Automatic detection of spindles and K-complexes in sleep EEG using switching multiple models, Biomedical Signal Processing and Control, 10 (2014) 117-127.
[9] T. Babaie, S. Chawla, R. Abeysuriya, Sleep analytics and online selective anomaly detection, in: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 2014, pp. 362-371.
[10] Z.R. Zamir, N. Sukhorukova, H. Amiel, A. Ugon, C. Philippe, Convex optimisation-based methods for k-complex detection, Applied Mathematics and Computation, 268 (2015) 947-956.
[11] A. Parekh, I.W. Selesnick, D.M. Rapoport, I. Ayappa, Detection of K-complexes and sleep spindles (DETOKS) using sparse optimization, Journal of neuroscience methods, 251 (2015) 37-46.
[12] I. Daubechies, J. Lu, H.-T. Wu, Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool, Applied and computational harmonic analysis, 30(2) (2011) 243-261.
[13] C. Yücelbaş, Ş. Yücelbaş, S. Özşen, G. Tezel, S. Küççüktürk, Ş. Yosunkaya, Automatic detection of sleep spindles with the use of STFT, EMD and DWT methods, Neural Computing and Applications, (2016) 1-17.
[14] G. Thakur, E. Brevdo, N.S. Fučkar, H.-T. Wu, The synchrosqueezing algorithm for time-varying spectral analysis: Robustness properties and new paleoclimate applications, Signal Processing, 93(5) (2013) 1079-1094.
[15] Z. Ghanbari, M.H. Moradi, Synchrosqueezing transform: Application in the analysis of the K-complex pattern, in: Biomedical Engineering and 2016 1st International Iranian Conference on Biomedical Engineering (ICBME), 2016 23rd Iranian Conference on, IEEE, 2016, pp. 221-225.
[16] M.M. Kabir, R. Tafreshi, D.B. Boivin, N. Haddad, Enhanced automated sleep spindle detection algorithm based on synchrosqueezing, Medical & biological engineering & computing, 53(7) (2015) 635-644.
[17] H.-T. Wu, Y.-H. Chan, Y.-T. Lin, Y.-H. Yeh, Using synchrosqueezing transform to discover breathing dynamics from ECG signals, Applied and Computational Harmonic Analysis, 36(2) (2014) 354-359.
[18] H.-T. Wu, S.-S. Hseu, M.-Y. Bien, Y.R. Kou, I. Daubechies, Evaluating physiological dynamics via synchrosqueezing: Prediction of ventilator weaning, IEEE Transactions on Biomedical Engineering, 61(3) (2014) 736-744.
[19] S. Devuyst, T. Dutoit, P. Stenuit, M. Kerkhofs, Automatic K-complexes detection in sleep EEG recordings using likelihood thresholds, in: Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE, IEEE, 2010, pp. 4658-4661.
[20] J.M. O’Toole, A. Temko, N. Stevenson, Assessing instantaneous energy in the EEG: a non-negative, frequency-weighted energy operator, in: Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, IEEE, 2014, pp. 3288-3291.
[21] L.K. Krohne, R.B. Hansen, J.A. Christensen, H.B. Sorensen, P. Jennum, Detection of K-complexes based on the wavelet transform, in: Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, IEEE, 2014, pp. 5450-5453.