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Multi-modal Signal Based Childhood Rolandic Epilepsy Detection

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Cognitive Systems and Information Processing (ICCSIP 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1515))

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Abstract

Electroencephalogram (EEG) is the common signal used in epilepsy analysis but suffered from the inconvenient issue in data acquisition, especially when applied to children with Rolandic epilepsy. In this paper, we present a study on multi-modal signal based epileptic seizure detection for children suffered from Rolandic epilepsy, where EEG combined with the synchronized surveillance video is included for analysis. The Mel frequency cepstrum coefficient (MFCC) and linear prediction cepstrum coefficient (LPCC) features are taken to characterize EEG. The spatiotemporal interest points (STIP) are extracted from video sequences to construct a bag of words model, which are based on the descriptors of histograms of oriented gradient (HOG) and the histograms of oriented optical flow (HOF) in the neighborhood. The histograms of word frequency (HWF) features are obtained for video representation. Direct feature fusion on the EEG based MFCC+LPCC and video based HWF is applied for classifier training. Data of 13 children with Rolandic epilepsy recorded from the Children’s Hospital, Zhejiang University School of Medicine (CHZU) are applied in the experiment. The results show that the model trained on the multi-modal features of EEG and video can achieve the highest overall accuracy of 98.2%.

Y. Wu and T. Jiang—Contribute equally to the paper.

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References

  1. Malarvili, M.B., Mesbah, M.: Newborn seizure detection based on heart rate variability. IEEE Trans. Biomed. Eng. 56(11), 2594–2603 (2009)

    Article  Google Scholar 

  2. Bailey, K.M., Im-Bolter, N.: Language and self-other differentiation in childhood epilepsy: a preliminary report. J. Child Fam. Stud. 28(4), 971–979 (2019)

    Article  Google Scholar 

  3. Cao, J., Hu, D., Wang, Y., Wang, J., Lei, B.: Epileptic classification with deep transfer learning based feature fusion algorithm. IEEE Trans. Cogn. Dev. Syst. (2021). https://doi.org/10.1109/TCDS.2021.3064228

  4. Hu, D., Cao, J., Lai, X., Wang, Y., Wang, S., Ding, Y.: Epileptic state classification by fusing hand-crafted and deep learning EEG features. IEEE Trans. Circuits Syst. II Exp. Briefs 68(4), 1542–1546 (2021). https://doi.org/10.1109/TCSII.2020.3031399

    Article  Google Scholar 

  5. Hu, D., Cao, J., Lai, X., Liu, J., Wang, S., Ding, Y.: Epileptic signal classification based on synthetic minority oversampling and blending algorithm. IEEE Trans. Cogn. Dev. Syst. 13, 368–382 (2020). https://doi.org/10.1109/TCDS.2020.3009020

    Article  Google Scholar 

  6. Cao, J., et al.: Unsupervised eye blink artifact detection from EEG with gaussian mixture model. IEEE J. Biomed. Health Inf. 25, 2895–2905 (2021). https://doi.org/10.1109/JBHI.2021.3057891

    Article  Google Scholar 

  7. Temko, A., Thomas, E., Marnane, W., Lightbody, G., Boylan, G.: EEEG-based neonatal seizure detection with support vector machines. Clin. Neurophysiol. 122(3), 464–473 (2011)

    Article  Google Scholar 

  8. James, D., Xie, X., Eslambolchilar, P.: A discriminative approach to automatic seizure detection in multichannel EEG signals. In: 2014 22nd European Signal Processing Conference (EUSIPCO), pp. 2010–2014. IEEE (2014)

    Google Scholar 

  9. Parvez, M.Z., Paul, M., Antolovich, M.: Detection of pre-stage of epileptic seizure by exploiting temporal correlation of EMD decomposed EEG signals. J. Med. Bioeng. 4(2), 1–7 (2015)

    Google Scholar 

  10. Abbasi, M.U., Rashad, A., Basalamah, A., Tariq, M.: Detection of epilepsy seizures in neo-natal EEG using LSTM architecture. IEEE Access 7, 179074–179085 (2019)

    Article  Google Scholar 

  11. Ansari, A.H., Cherian, P.J., Caicedo, A., Naulaers, G., De Vos, M., Van Huffel, S.: Neonatal seizure detection using deep convolutional neural networks. Int. J. Neural Syst. 29(4), 1850011 (2019)

    Article  Google Scholar 

  12. Cao, J., Zhu, J., Hu, W., Kummert, A.: Epileptic signal classification with deep EEG features by stacked CNNs. IEEE Trans. Cogn. Dev. Syst. 12(4), 709–722 (2019)

    Article  Google Scholar 

  13. Geertsema, E.E., et al.: Automated video-based detection of nocturnal convulsive seizures in a residential care setting. Epilepsia 59, 53–60 (2018)

    Article  Google Scholar 

  14. Ntonfo, G.M.K., Ferrari, G., Raheli, R., Pisani, F.: Low-complexity image processing for real-time detection of neonatal clonic seizures. IEEE Trans. Inf Technol. Biomed. 16(3), 375–382 (2012)

    Article  Google Scholar 

  15. Lu, H., Pan, Y., Mandal, B., Eng, H.-L., Guan, C., Chan, D.W.: Quantifying limb movements in epileptic seizures through color-based video analysis. IEEE Trans. Biomed. Eng. 60(2), 461–469 (2012)

    Article  Google Scholar 

  16. Cuppens, K., Lagae, L., Ceulemans, B., Van Huffel, S., Vanrumste, B.: Automatic video detection of body movement during sleep based on optical flow in pediatric patients with epilepsy. Med. Biol. Eng. Comput. 48(9), 923–931 (2010)

    Article  Google Scholar 

  17. Cuppens, K., et al.: Using spatio-temporal interest points (STIP) for myoclonic jerk detection in nocturnal video. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4454–4457. IEEE (2012)

    Google Scholar 

  18. Karayiannis, N.B., Tao, G., Frost, J.D., Jr., Wise, M.S., Hrachovy, R.A., Mizrahi, E.M.: Automated detection of videotaped neonatal seizures based on motion segmentation methods. Clin. Neurophysiol. 117(7), 1585–1594 (2006)

    Article  Google Scholar 

  19. Achilles, F., Tombari, F., Belagiannis, V., Loesch, A.M., Noachtar, S., Navab, N.: Convolutional neural networks for real-time epileptic seizure detection. Comput. Meth. Biomech. Biomed. Eng. Imaging Vis. 6(3), 264–269 (2018)

    Article  Google Scholar 

  20. Yang, Y., Sarkis, R., El Atrache, R., Loddenkemper, T., Meisel, C.: Video-based detection of generalized tonic-clonic seizures using deep learning. IEEE J. Biomed. Health Inform. 25, 2997–3008 (2021)

    Article  Google Scholar 

  21. Mporas, I., Tsirka, V., Zacharaki, E.I., Koutroumanidis, M., Megalooikonomou, V.: Online seizure detection from EEG and ECG signals for monitoring of epileptic patients. In: Likas, A., Blekas, K., Kalles, D. (eds.) SETN 2014. LNCS (LNAI), vol. 8445, pp. 442–447. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07064-3_37

    Chapter  Google Scholar 

  22. Mporas, I., Tsirka, V., Zacharaki, E.I., Koutroumanidis, M., Richardson, M., Megalooikonomou, V.: Seizure detection using EEG and ECG signals for computer-based monitoring, analysis and management of epileptic patients. Exp. Syst. Appl. 42(6), 3227–3233 (2015)

    Article  Google Scholar 

  23. Milošević, M., et al.: Automated detection of tonic-clonic seizures using 3-d accelerometry and surface electromyography in pediatric patients. IEEE J. Biomed. Health Inform. 20(5), 1333–1341 (2015)

    Article  Google Scholar 

  24. Aghaei, H., Kiani, M.M., Aghajan, H.: Epileptic seizure detection based on video and EEG recordings. In: 2017 IEEE Biomedical Circuits and Systems Conference (BioCAS), pp. 1–4. IEEE (2017)

    Google Scholar 

  25. Alving, J., Beniczky, S.: Diagnostic usefulness and duration of the inpatient long-term video-EEG monitoring: findings in patients extensively investigated before the monitoring. Seizure 18(7), 470–473 (2009)

    Article  Google Scholar 

  26. Velis, D., Plouin, P., Gotman, J., da Silva, F.L., ILAE DMC Subcommittee on Neurophysiology: Recommendations regarding the requirements and applications for long-term recordings in epilepsy (2007)

    Google Scholar 

  27. Rubboli, G., et al.: A European survey on current practices in epilepsy monitoring units and implications for patients’ safety. Epilepsy Behav. 44, 179–184 (2015)

    Article  Google Scholar 

  28. Handayani, D., Yaacob, H., Wahab, A., Alshaikli, I.F.T.: Statistical approach for a complex emotion recognition based on EEG features. In: 2015 4th International Conference on Advanced Computer Science Applications and Technologies (ACSAT), pp. 202–207 (2015). https://doi.org/10.1109/ACSAT.2015.54

  29. Antoniol, G., Rollo, V.F., Venturi, G.: Linear predictive coding and cepstrum coefficients for mining time variant information from software repositories. In: Proceedings of the 2005 International Workshop on Mining Software Repositories, pp. 1–5 (2005)

    Google Scholar 

  30. Laptev, I.: On space-time interest points. Int. J. Comput. Vis. 64(2–3), 107–123 (2005)

    Article  Google Scholar 

  31. Fernández, A., Garcia, S., Herrera, F., Chawla, N.V.: Smote for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. J. Artif. Intell. Res. 61, 863–905 (2018)

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (2021YFE0100100), the National Natural Science Foundation of China (U1909209), the Open Research Projects of Zhejiang Lab (2021MC0AB04), the Key Research and Development Program of Zhejiang Province (2020C03038), and the Zhejiang Provincial Natural Science Foundation (LBY21H090002).

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Authors and Affiliations

  1. Machine Learning and I-health International Cooperation Base of Zhejiang Province, Hangzhou Dianzi University, Hangzhou, 310018, China

    Yixian Wu, Dinghan Hu & Jiuwen Cao

  2. Artificial Intelligence Institute, Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China

    Yixian Wu, Dinghan Hu & Jiuwen Cao

  3. Department of Neurology, The Children’s Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, Zhejiang, China

    Tiejia Jiang & Feng Gao

Authors
  1. Yixian Wu
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  2. Dinghan Hu
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  3. Tiejia Jiang
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  4. Feng Gao
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  5. Jiuwen Cao
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Corresponding author

Correspondence to Jiuwen Cao .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Fuchun Sun

  2. National University of Defense Technology, Changsha, China

    Dewen Hu

  3. Universität Hamburg, Hamburg, Germany

    Stefan Wermter

  4. Tsingzhan Artificial Intelligence Research Institute, Nanjing, China

    Lei Yang

  5. Tsinghua University, Beijing, China

    Huaping Liu

  6. Tsinghua University, Beijing, China

    Bin Fang

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Wu, Y., Hu, D., Jiang, T., Gao, F., Cao, J. (2022). Multi-modal Signal Based Childhood Rolandic Epilepsy Detection. In: Sun, F., Hu, D., Wermter, S., Yang, L., Liu, H., Fang, B. (eds) Cognitive Systems and Information Processing. ICCSIP 2021. Communications in Computer and Information Science, vol 1515. Springer, Singapore. https://doi.org/10.1007/978-981-16-9247-5_39

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  • DOI: https://doi.org/10.1007/978-981-16-9247-5_39

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-9246-8

  • Online ISBN: 978-981-16-9247-5

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