Application of Frequency Features of Optical Flow for Event Detection in Video-EEG Monitoring Data

Dmitry Murashov (Login required)
Federal Research Center “Computer Science and Control” of RAS, Moscow, Russia

Yury Obukhov
Kotel'nikov Institute of Radio Engineering and Electronics of RAS, Moscow, Russia

Ivan Kershner
Kotel'nikov Institute of Radio Engineering and Electronics of RAS, Moscow, Russia

Mikhail Sinkin
N. V. Sklifosovsky Research Institute for Emergency Medicine of Moscow Healthcare Department, Moscow, Russia
A. I. Yevdokimov Moscow State University of Medicine and Dentistry, Moscow, Russia


Paper #3432 received 25 May 2021; revised manuscript received 12 Jul 2021; accepted for publication 14 Jul 2021; published online 30 Sep 2021.

DOI: 10.18287/JBPE21.07.030301

Abstract

The work is devoted to the study of the frequency features of the optical flow obtained from the video record of long-term video-electroencephalographic (video-EEG) monitoring data of patients with epilepsy. It is necessary to obtain features to recognize epileptic seizures and differentiate them from non-epileptic events. We propose to analyze the periodograms of the smoothed optical flow computed from the fragments of the patient’s video recordings. We use Welch's method to obtain periodograms. The values of the power spectral density of the optical flow at the selected frequencies are used as features. Using the clustering algorithm, seven groups of events are identified in video recordings and combined into three generalized classes. We train SVM classifier and conduct recognition of events in a test sample of 103 video fragments in four patients. The experiment indicates the accuracy of event classification equal to 90.3%.

Keywords

video-electroencephalographic monitoring; optical flow; periodogram; Welch’s method; clustering; classification

Full Text:

PDF

References


1. M. Patel, P.Satishchandra, J. Saini, R. D. Bharath, and S. Sinha, “Eating epilepsy: Phenotype, MRI, SPECT and video-EEG observations,” Epilepsy Research 107(1–2), 115–120 (2013).

2. T. Chen, Y. Si, D. Chen, L. Zhu, D. Xu, S. Chen, D. Zhou, and L. Liu, “The value of 24-hour video-EEG in evaluating recurrence risk following a first unprovoked seizure: A prospective study,” Seizure 40, 46–51 (2016).

3. N. B. Karayiannis, S. Srinivasan, R. Bhattacharya, M. S. Wise, J. D. Frost, and E. M. Mizrahi, “Extraction of motion strength and motor activity signals from video recordings of neonatal seizures,” IEEE Transactions on Medical Imaging 20(9), 965–980 (2001).

4. N. B. Karayiannis, G. Tao, “Improving the extraction of temporal motion strength signals from video recordings of neonatal seizures,” In Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 22 July 2003, Miami, FL, USA, 87–92 (2003).

5. N. B. Karayiannis, A. Sami, J .D. Frost, M. S. Wise, and E. M. Mizrahi, “Automated extraction of temporal motor activity signals from video recordings of neonatal seizures based on adaptive block matching,” IEEE Transactions on Biomedical Engineering 52(4), 676–686 (2005).

6. G. M. Kouamou Ntonfo, G. Ferrari, R. Raheli, and F. Pisani, “Low-Complexity Image Processing for Real-Time Detection of Neonatal Clonic Seizures,” IEEE Transactions on Information Technology in Biomedicine 16(3), 375–382 (2012).

7. L. Cattani, D. Alinovi, G. Ferrari, R. Raheli, E. Pavlidis, C. Spagnoli, and F. Pisani, “Monitoring infants by automatic video processing: A unified approach to motion analysis,” Computers in Biology and Medicine 80, 158–165 (2017).

8. E. E. Geertsema, R. D. Thijs, T. Gutter, B. Vledder, J. B. Arends, F. S. Leijten, G. H. Visser, and S. N. Kalitzin, “Automated video-based detection of nocturnal convulsive seizures in a residential care setting,” Epilepsia 59(S1), 53–60 (2018).

9. S. Kalitzin, G. Petkov, D. Velis, B. Vledder, and F. L. da Silva, “Automatic Segmentation of Episodes Containing Epileptic Clonic Seizures in Video Sequences,” IEEE Transactions on Biomedical Engineering 59(12), 3379–3385 (2012).

10. E. E. Geertsema, G. H. Visser, J. W. Sander, and S. N. Kalitzin, “Automated non-contact detection of central apneas using video,” Biomedical Signal Processing and Control 55, 101658 (2020).

11. A. van Westrhenen, G. Petkov, S. N. Kalitzin, R. H. C. Lazeron, and R. D. Thijs, “Automated video-based detection of nocturnal motor seizures in children,” Epilepsia 61(S1), S36–S40 (2020).

12. D. Murashov, Yu. Obukhov, I. Kershner, and M. Sinkin, “Detecting Events in Video Sequence Of Video-EEG Monitoring,” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 42(2/W12), 155-159 (2019).

13. D. Murashov, Yu. Obukhov, I. Kershner, and M. Sinkin, “A technique for detecting diagnostic events in video channel of synchronous video and electroencephalographic monitoring data,” CEUR Workshop Proceedings 2391, 285–292 (2019).

14. D. M. Murashov, Y. V. Obukhov, I. A. Kershner, and M. V. Sinkin, “An algorithm for detecting events in video EEG monitoring data of patients with craniocerebral injuries,” Computer Optics 45(2), 301-305 (2021).

15. K. Obukhov, I. Kersher, I. Komoltsev, and Yu. Obukhov, “Epileptiform Activity Detection and Classification Algorithms of Rats with Post-traumatic Epilepsy,” Pattern Recognition and Image Analysis 28(2), 346-353 (2018).

16. B. D. Lucas, T. Kanade, “An iterative image registration technique with an application to stereo vision,” Proceedings of Imaging Understanding Workshop, 121-130 (1981).

17. R. E. Kalman, R. S. Bucy, “New results in linear filtering and prediction theory,” Journal of basic engineering 83(1), 95-108 (1961).

18. S. L. Jr. Marple , Digital spectral analysis with applications, Prentice-Hall, Inc., Englewood Cliffs, NJ (1987).

19. Yu. I. Zhuravlev, V. V. Ryazanov, and O. V. Sen’ko, Recognition, Mathematical methods. Software system. Practical applications, Phasis, Moscow (2006) [In Russian].

20. R. O. Duda, P. E. Hart, and D. Stork, Pattern classification, 2nd ed., A Wiley-Interscience Publication, New York (2001).

21. J. Dobesberger, G. Walser, I. Unterberger, K. Seppi, G. Kuchukhidze, J. Larch, G. Bauer, T. Bodner, T. Falkenstetter, M. Ortler, G. Luef, and E. Trinka, “Video-EEG monitoring: safety and adverse events in 507 consecutive patients,” Epilepsia 52(3), 443-52 (2011).




Bookmark and Share


© 2014-2025 Authors
Public Media Certificate (RUS). 12+