Prediction of seizures in sleep based on power spectrum_Journal of Biomedical Engineering

Authors：

LIU Weinan ^1,2 , LIU Yan ^1,3 , TONG Baotong ¹ , ZHAO Lingxiao ¹ , YANG Yingxue ^4,5 , WANG Yuping ^4,5 ,  DAI Yakang ¹

1. Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of Sciences, Suzhou, jiangsu 215163, P.R.China;
2. University of Chinese Academy of Sciences, Beijing 100049, P.R.China;
3. Harbin University of Science and Technology, Harbin 150080, P.R.China;
4. Xuanwu Hospital Capital Medical University, Beijing 100053, P.R.China;
5. Beijing Key Laboratory of Neuromodulatio, Beijing 100053, P.R.China;

Corresponding author：

DAI Yakang, Email: daiyk@sibet.ac.cn

Keywords：

seizure prediction; electroencephalogram signals; power spectrum; support vector machine

DOI：

10.7507/1001-5515.201708062

Video：

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Abstract Full text Figures/Tables Video References Cited by

Seizures during sleep increase the probability of complication and sudden death. Effective prediction of seizures in sleep allows doctors and patients to take timely treatments to reduce the aforementioned probability. Most of the existing methods make use of electroencephalogram (EEG) to predict seizures, which are not specific developed for the sleep. However, EEG during sleep has its characteristics compared with EEG during other states. Therefore, in order to improve the sensitivity and reduce the false alarm rate, this paper utilized the characteristics of EEG to predict seizures during sleep. We firstly constructed the feature vector including the absolute power spectrum, the relative power spectrum and the power spectrum ratio in different frequencies. Secondly, the separation criterion and branch-and-bound method were applied to select features. Finally, support vector machine classifier were trained, which is then employed for online prediction. Compared with the existing method that do not consider the characteristics of sleeping EEG (sensitivity 91.67%, false alarm rate 9.19%), the proposed method was superior in terms of sensitivity (100%) and false alarm rate (2.11%). This method can improve the existing epilepsy prediction methods and has important clinical value.

Citation： LIU Weinan, LIU Yan, TONG Baotong, ZHAO Lingxiao, YANG Yingxue, WANG Yuping, DAI Yakang. Prediction of seizures in sleep based on power spectrum. Journal of Biomedical Engineering, 2018, 35(3): 329-336. doi: 10.7507/1001-5515.201708062 Copy

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7.	Shoeb A, Edwards H, Connolly J, et al. Patient-specific seizure onset detection. Epilepsy Behav, 2004, 5(4): 483-498.
8.	Fathima T, Bedeeuzzaman M, Farooq O, et al. Wavelet based features for epileptic seizure detection. MES Journal of Technology and Management, 2011, 2(1): 108-112.
9.	Park Y, Luo Lan, Parhi K K, et al. Seizure prediction with spectral power of EEG using cost-sensitive support vector machines. Epilepsia, 2011, 52(10): 1761-1770.
10.	Alarcon G, Binnie C D, Elwes R D, et al. Power spectrum and intracranial EEG patterns at seizure onset in partial epilepsy. Electroencephalogr Clin Neurophysiol, 1995, 94(5): 326-337.
11.	Bandarabadi M, Teixeira C A, Rasekhi J, et al. Epileptic seizure prediction using relative spectral power features. Clin Neurophysiol, 2015, 126(2): 237-248.
12.	Fergus P, Hussain A, Hignett D, et al. A machine learning system for automated whole-brain seizure detection. Applied Computing & Informatics, 2016, 12(1): 70-89.
13.	Lyu M R, Wang N. Exploration of instantaneous amplitude and frequency features for epileptic seizure prediction// IEEE 12th International Conference on Bioinformatics & Bioengineering, Larnaca, 2012: 292-297.
14.	Goldberger A L, Amaral L A, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 2000, 101(23): E215-E220.
15.	Worrell G A, Parish L, Cranstoun S D, et al. High-frequency oscillations and seizure generation in neocortical epilepsy. Brain, 2004, 127(Pt 7): 1496-1506.
16.	Bandarabadi M, Teixeira C A, Netoff T I, et al. Robust and low complexity algorithms for seizure detection//36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago,2014: 4447-4450.
17.	Kariwala V, Ye Lingjian, Cao Yi. Branch and bound method for regression-based controlled variable selection. Comput Chem Eng, 2013, 54: 1-7.

1. Langan Y, Nashef L, Sander J W. Case-control study of SUDEP. Neurology, 2005, 64(7): 1131-1133.
2. Cuppens K, Lagae L, Ceulemans B, et al. Automatic video detection of body movement during sleep based on optical flow in pediatric patients with epilepsy. Med Biol Eng Comput, 2010, 48(9): 923-931.
3. Salem O, Rebhi Y, Boumaza A, et al. Detection of nocturnal epileptic seizures using wireless 3-D accelerometer sensors//IEEE 16th International Conference on E-Health Networking, Applications and Services. Natal, 2014: 237-242.
4. Fulton S, Poppel K V, Mcgregor A, et al. Prospective study of 2 bed alarms for detection of nocturnal seizures. J Child Neurol, 2013, 28(11): 1430-1433.
5. 刘晓燕. 临床脑电图学. 北京: 人民卫生出版社, 2006: 126-127.
6. Giannakakis G, Sakkalis V, Pediaditis M, et al. Methods for seizure detection and prediction: an overview. Sakkalis V. Modern Electroencephalographic Assessment Techniques, New York, Hymana Press, 2014: 131-157.
7. Shoeb A, Edwards H, Connolly J, et al. Patient-specific seizure onset detection. Epilepsy Behav, 2004, 5(4): 483-498.
8. Fathima T, Bedeeuzzaman M, Farooq O, et al. Wavelet based features for epileptic seizure detection. MES Journal of Technology and Management, 2011, 2(1): 108-112.
9. Park Y, Luo Lan, Parhi K K, et al. Seizure prediction with spectral power of EEG using cost-sensitive support vector machines. Epilepsia, 2011, 52(10): 1761-1770.
10. Alarcon G, Binnie C D, Elwes R D, et al. Power spectrum and intracranial EEG patterns at seizure onset in partial epilepsy. Electroencephalogr Clin Neurophysiol, 1995, 94(5): 326-337.
11. Bandarabadi M, Teixeira C A, Rasekhi J, et al. Epileptic seizure prediction using relative spectral power features. Clin Neurophysiol, 2015, 126(2): 237-248.
12. Fergus P, Hussain A, Hignett D, et al. A machine learning system for automated whole-brain seizure detection. Applied Computing & Informatics, 2016, 12(1): 70-89.
13. Lyu M R, Wang N. Exploration of instantaneous amplitude and frequency features for epileptic seizure prediction// IEEE 12th International Conference on Bioinformatics & Bioengineering, Larnaca, 2012: 292-297.
14. Goldberger A L, Amaral L A, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 2000, 101(23): E215-E220.
15. Worrell G A, Parish L, Cranstoun S D, et al. High-frequency oscillations and seizure generation in neocortical epilepsy. Brain, 2004, 127(Pt 7): 1496-1506.
16. Bandarabadi M, Teixeira C A, Netoff T I, et al. Robust and low complexity algorithms for seizure detection//36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago,2014: 4447-4450.
17. Kariwala V, Ye Lingjian, Cao Yi. Branch and bound method for regression-based controlled variable selection. Comput Chem Eng, 2013, 54: 1-7.

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