Portable Epileptic Seizure Monitoring Intelligent System Based on Android System_Journal of Biomedical Engineering

Authors：

 LIANGZhenhu ¹ , WUShufeng ¹ , YANGChunlin ¹ , JIANGZhenzhou ¹ , YUTao ² , LUChengbiao ¹ , LIXiaoli ¹

1. Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China;
2. Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, China;

Corresponding author：

LIANGZhenhu, Email: zhl@ysu.edu.cn

Keywords：

epileptic seizure monitoring; Android smart phone; multi-scale permutation entropy; portable wearable system

DOI：

10.7507/1001-5515.20160007

Video：

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The clinical electroencephalogram (EEG) monitoring systems based on personal computer system can not meet the requirements of portability and home usage. The epilepsy patients have to be monitored in hospital for an extended period of time, which imposes a heavy burden on hospitals. In the present study, we designed a portable 16-lead networked monitoring system based on the Android smart phone. The system uses some technologies including the active electrode, the WiFi wireless transmission, the multi-scale permutation entropy (MPE) algorithm, the back-propagation (BP) neural network algorithm, etc. Moreover, the software of Android mobile application can realize the processing and analysis of EEG data, the display of EEG waveform and the alarm of epileptic seizure. The system has been tested on the mobile phones with Android 2.3 operating system or higher version and the results showed that this software ran accurately and steadily in the detection of epileptic seizure. In conclusion, this paper provides a portable and reliable solution for epileptic seizure monitoring in clinical and home applications.

Citation： LIANGZhenhu, WUShufeng, YANGChunlin, JIANGZhenzhou, YUTao, LUChengbiao, LIXiaoli. Portable Epileptic Seizure Monitoring Intelligent System Based on Android System. Journal of Biomedical Engineering, 2016, 33(1): 31-37. doi: 10.7507/1001-5515.20160007 Copy

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4.	MARSAN C A, ZIVIN L S. Factors related to the occurrence of typical paroxysmal abnormalities in the EEG records of epileptic patients[J]. Epilepsia, 1970, 11(4): 361-381.
5.	王晓韡, 石立臣, 吕宝粮. 干电极脑电采集技术综述[J]. 中国生物医学工程学报, 2010, 29(5): 777-784.
6.	KIM D G, ROH Y W, HONG K S. A portable EEG signal acquisition system[J]. Advanced Science Letters, 2012, 14(1): 222-226.
7.	BANDT C, POMPE B. Permutation entropy: a natural complexity measure for time series[J]. Phys Rev Lett, 2002, 88(17): 174102-1-174102-4.
8.	OUYANG G, LI Jing, LIU Xianzeng, et al. Dynamic characteristics of absence EEG recordings with multiscale permutation entropy analysis[J]. Epilepsy Res, 2013, 104(3): 246-252.
9.	XU Jiawei, YAZICIOGLU R F, GRUNDLEHNER B A, et al. A 160μW 8-channel active electrode system for EEG monitoring[J]. IEEE Trans Biomed Circuits Syst, 2011, 5(6): 555-567.
10.	MCCLELLAN J, PARKS T. A unified approach to the design of optimum FIR linear-phase digital filters[J]. IEEE Trans Circuit Theory, 1973, 20(6): 697-701.
11.	WIDROW B, GLOVER J R Jr, MCCOOL J M, et al. Adaptive noise cancelling: Principles and applications[J]. Proceedings of the IEEE, 1975, 63(12): 1692-1716.
12.	TONNER P H, BEIN B. Classic electroencephalographic parameters: median frequency, spectral edge frequency etc[J]. Best Pract Res Clin Anaesthesiol, 2006, 20(1): 147-159.
13.	ANDRZEJAK R G, LEHNERTZ K, MORMANN F, et al. Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state[J]. Phys Rev E Stat Nonlin Soft Matter Phys, 2001, 64(6 Pt 1): 061907-1-061907-8.

1. 秦兵, 廖卫平, 臼井直敬, 等. 癫痫:全球重点防治的神经精神疾病[J]. 实用医学杂志, 2012, 28(13): 2109-2111.
2. PIERELLI F, CHATRIAN G E, ERDLY W W, et al. Long-term EEG-video-audio monitoring: Detection of partial epileptic seizures and psychogenic episodes by 24-hour EEG record review[J]. Epilepsia, 1989, 30(5): 513-523.
3. GARRY H, MCGINLEY B, JONES E, et al. An evaluation of the effects of wavelet coefficient quantisation in transform based EEG compression[J]. Computers in Biology and Medicine, 2013, 43(6): 661-669.
4. MARSAN C A, ZIVIN L S. Factors related to the occurrence of typical paroxysmal abnormalities in the EEG records of epileptic patients[J]. Epilepsia, 1970, 11(4): 361-381.
5. 王晓韡, 石立臣, 吕宝粮. 干电极脑电采集技术综述[J]. 中国生物医学工程学报, 2010, 29(5): 777-784.
6. KIM D G, ROH Y W, HONG K S. A portable EEG signal acquisition system[J]. Advanced Science Letters, 2012, 14(1): 222-226.
7. BANDT C, POMPE B. Permutation entropy: a natural complexity measure for time series[J]. Phys Rev Lett, 2002, 88(17): 174102-1-174102-4.
8. OUYANG G, LI Jing, LIU Xianzeng, et al. Dynamic characteristics of absence EEG recordings with multiscale permutation entropy analysis[J]. Epilepsy Res, 2013, 104(3): 246-252.
9. XU Jiawei, YAZICIOGLU R F, GRUNDLEHNER B A, et al. A 160μW 8-channel active electrode system for EEG monitoring[J]. IEEE Trans Biomed Circuits Syst, 2011, 5(6): 555-567.
10. MCCLELLAN J, PARKS T. A unified approach to the design of optimum FIR linear-phase digital filters[J]. IEEE Trans Circuit Theory, 1973, 20(6): 697-701.
11. WIDROW B, GLOVER J R Jr, MCCOOL J M, et al. Adaptive noise cancelling: Principles and applications[J]. Proceedings of the IEEE, 1975, 63(12): 1692-1716.
12. TONNER P H, BEIN B. Classic electroencephalographic parameters: median frequency, spectral edge frequency etc[J]. Best Pract Res Clin Anaesthesiol, 2006, 20(1): 147-159.
13. ANDRZEJAK R G, LEHNERTZ K, MORMANN F, et al. Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state[J]. Phys Rev E Stat Nonlin Soft Matter Phys, 2001, 64(6 Pt 1): 061907-1-061907-8.

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