Epileptic seizure detection with Convolutional Neural Networks and the Continuous Wavelet Transform
Resumo
The study of epileptic seizure often involves animal models to simulate the human behavior. Such models demand monitoring the evolution of the animal behavior continuously. Detecting seizure in this setup remains a challenge, because it typically requires trained personnel to annotate video sequences looking for the timestamps of seizure events. Deep Learning methods can help to solve this task in a more automatic and efficient manner due to their capacity of retrieving patterns from data. In this work, we conducted a pilot study to detect epileptic seizure from the images of small rodents using Convolutional Neural Networks (CNN) and the Continuous Wavelet Transform (CWT). We used the Social LEAP Estimates Animal Poses (SLEAP) framework for animal recognition to extract the morphological skeleton. Then, our CWT-CNN method used information of the frequency, magnitude and temporal evolution of head and thorax displacements to classify the animal behavior. The results showed a mean accuracy of 82.7%in the classification of epileptic seizure events.
Referências
W. A. T. E. A. C. M. S. S. J. C. Z. K. L. Turski, "Limbic seizures produced by pilocarpine in rats: Behavioural, electroencephalographic and neuropathological study," Behavioural Brain Research, vol. 9, pp. 315-335, 1983.
R. J. Racine, "Modification of seizure activity by electrical stimulation: Ii. motor seizure," Electroencephalography and Clinical Neurophysiology, vol. 32, pp. 0-294, 1972.
G. Curia, D. Longo, G. Biagini, R. S. Jones, and M. Avoli, "The pilocarpine model of temporal lobe epilepsy," Journal of Neuroscience Methods, vol. 172, no. 2, pp. 143-157, 2008.
V. C. B. K. D. N. S. K. A. F. D. E.-G. S. W. S. Antonopoulos, Ioannis; Robu, "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, vol. 130, p. 109899, 2020.
J. W. M. M. Pereira, Talmo D.; Shaevitz, "Quantifying behavior to understand the brain," Nature Neuroscience, 2020.
U. Klibaite and J. W. Shaevitz, "Paired fruit flies synchronize behavior: Uncovering social interactions in drosophila melanogaster," PLOS 251 Computational Biology, vol. 16, pp. 1-21, 10 2020.
D. E. W. L. K. M. W. S. S.-H. M.-M. S. J. W. Pereira, Talmo D.; Aldarondo, "Fast animal pose estimation using deep neural networks," Nature Chemical Biology, vol. 16, pp. 117-125, 2019.
S. Günel, H. Rhodin, D. Morales, J. Campagnolo, P. Ramdya, and P. Fua, "Deepfly3d, a deep learning-based approach for 3d limb and appendage tracking in tethered, adult Drosophila," eLife, vol. 8, p. e48571, oct 2019.
P. C. K. M. A. T. M. V. N. M.-M. W. B. M. Mathis, Alexander; Mamidanna, "Deeplabcut: markerless pose estimation of user-defined body parts with deep learning," Nature Neuroscience, 2018.
T. D. Pereira, N. Tabris, A. Matsliah, D. M. Turner, J. Li, S. Ravindranath, E. S. Papadoyannis, E. Normand, D. S. Deutsch, Z. Y. Wang, G. C. McKenzie-Smith, C. C. Mitelut, M. D. Castro, J. D'Uva, M. Kislin, D. H. Sanes, S. D. Kocher, S. S-H, A. L. Falkner, J. W. Shaevitz, and M. Murthy, "Sleap: A deep learning system for multi-animal pose tracking," Nature Methods, vol. 19, no. 4, 2022.
J. M. Graving, D. Chae, H. Naik, L. Li, B. Koger, B. R. Costelloe, and I. D. Couzin, "Deepposekit, a software toolkit for fast and robust animal pose estimation using deep learning," eLife, vol. 8, p. e47994, oct 2019.
X. M. H. R. Z. O. W. W. K. Ma, "The use of the mexican hat and the morlet wavelets for detection of ecological patterns," Plant Ecology, vol. 179, pp. 1-19, 2005.
A. F. Agarap, "Deep learning using rectified linear units (relu)," arXiv preprint arXiv:1803.08375, 2018.
R. W. Schafer et al., "What is a savitzky-golay filter," IEEE Signal processing magazine, vol. 28, no. 4, pp. 111-117, 2011.
P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, S. J. van der Walt, M. Brett, J. Wilson, K. J. Millman, N. Mayorov, A. R. J. Nelson, E. Jones, R. Kern, E. Larson, C. J. Carey, I. Polat, Y. Feng, E. W. Moore, J. VanderPlas, D. Laxalde, J. Perktold, R. Cimrman, I. Henriksen, E. A. Quintero, C. R. Harris, A. M. Archibald, A. H. Ribeiro, F. Pedregosa, P. van Mulbregt, and SciPy 1.0 Contributors, "SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python," Nature Methods, vol. 17, pp. 261-272, 2020.
