Portable epileptic seizure monitoring intelligent system based on Android system

被引:0
|
作者
Liang Z. [1 ]
Wu S. [1 ]
Yang C. [1 ]
Jiang Z. [1 ]
Yu T. [2 ]
Lu C. [1 ]
Li X. [1 ]
机构
[1] Institute of Electrical Engineering, Yanshan University, Qinhuangdao
[2] Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing
来源
Liang, Zhenhu (zhl@ysu.edu.cn) | 1600年 / West China Hospital, Sichuan Institute of Biomedical Engineering卷 / 33期
关键词
Android smart phone; Epileptic seizure monitoring; Multi-scale permutation entropy; Portable wearable system;
D O I
10.7507/1001-5515.20160007
中图分类号
学科分类号
摘要
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. © 2016, Editorial Office of Journal of Biomedical Engineering. All right reserved.
引用
收藏
页码:31 / 37
页数:6
相关论文
共 11 条
  • [1] 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, Epilepsia, 30, 5, pp. 513-523, (1989)
  • [2] Garry H., Mcginley B., Jones E., Et al., An evaluation of the effects of wavelet coefficient quantisation in transform based EEG compression, Computers in Biology and Medicine, 43, 6, pp. 661-669, (2013)
  • [3] Marsan C.A., Zivin L.S., Factors related to the occurrence of typical paroxysmal abnormalities in the EEG records of epileptic patients, Epilepsia, 11, 4, pp. 361-381, (1970)
  • [4] Kim D.G., Roh Y.W., Hong K.S., A portable EEG signal acquisition system, Advanced Science Letters, 14, 1, pp. 222-226, (2012)
  • [5] Bandt C., Pompe B., Permutation entropy: A natural complexity measure for time series, Phys Rev Lett, 88, 17, (2002)
  • [6] Ouyang G., Li J., Liu X., Et al., Dynamic characteristics of absence EEG recordings with multiscale permutation entropy analysis, Epilepsy Res, 104, 3, pp. 246-252, (2013)
  • [7] Xu J., Yazicioglu R.F., Grundlehner B.A., Et al., A 160μ W 8-channel active electrode system for EEG monitoring, IEEE Trans Biomed Circuits Syst, 5, 6, pp. 555-567, (2011)
  • [8] Mcclellan J., Parks T., A unified approach to the design of optimum FIR linear-phase digital filters, IEEE Trans Circuit Theory, 20, 6, pp. 697-701, (1973)
  • [9] Widrow B., Glover J.R., Mccool J.M., Et al., Adaptive noise cancelling: Principles and applications, Proceedings of the IEEE, 63, 12, pp. 1692-1716, (1975)
  • [10] Tonner P.H., Bein B., Classic electroencephalographic parameters: Median frequency, spectral edge frequency etc, Best Pract Res Clin Anaesthesiol, 20, 1, pp. 147-159, (2006)