A Hardware and Mobile-Health Based System for the Analysis of EEG Signals

Abstract

Instantaneous monitoring of EEG signals is very important for patient follow up. Independent follow-up systems are needed for the physician to monitor and diagnose the patient continuously. In this article, a real-time design for an FIR (Finite Impulse Response) filter was presented using a cosh window function implemented on an FPGA (Field Programmable Gate Array) environment. The reason for using the cosh window is that it has better ripple ratio and larger sidelobe roll-off ratio than other windows in literature.  Since cosh window parameters can be changed in the developed design, they can be easily adapted to the new state change. After filtering the raw EEG signals, they were converted into a form that could be interpreted by a specialist physician. The filtered data was uploaded to a server on the internet so that the physician could access the EEG signals remotely via a mobile phone. The proposed system facilitated examination of the patient by the physician and made it possible to help instantly diagnose any illness.

Keywords

• FPGA,FIR filter,cosh window,EEG,android application

EEG Sinyallerinin Analizi için Donanım ve Mobil-Sağlık Tabanlı Bir Sistem

Öz

EEG sinyallerinin anlık olarak izlenmesi hasta takibi açısından son derece önemlidir. Hekimin hastayı sürekli olarak izlemesi ve teşhis yapabilmesi için mekândan bağımsız takip sistemlerine ihtiyaç vardır. Bu makalede, FPGA ortamında cosh pencere fonksiyonu kullanılarak FIR (Finite Impulse Response) filtrenin gerçek zamanlı tasarımı sunulmuştur.  Alınan ham EEG sinyalleri filtrelendikten sonra bu sinyaller uzman hekim tarafından yorumlayabilecek şekle dönüştürülmüştür. Filtrelenmiş veriler internet üzerindeki servera aktarılmış böylece, hekimin uzaktan mobil telefon yardımıyla EEG sinyallerine ulaşması sağlanmıştır. Önerilen bu sistem sayesinde hekimin hastayı sorgulaması kolaylaşmış ve anlık olarak hastalık teşhisi yapabilmesine imkân sunulmuştur.

Anahtar Kelimeler

• FPGA,FIR filtre,cosh penceresi,EEG,Android Uygulaması

References

1. 1- Kocadagli, O., Langari, R., Classification of EEG signals for epileptic seizures using hybrid artificial neural networks based wavelet transforms and fuzzy relatio, Expert Systems with Applications, 2017, 88, (1), p. 419-434.
2. 2- Vaewpanich, J., Reuter-Rice, K., Continuous electroencephalography in pediatric traumatic brain injury: Seizure characteristics and outcomes, Epilepsy & Behavior, 2016, 62, p, 225-230.
3. 3- A.Barone, D., Chokroverty S., Neurologic Diseases and Sleep, Sleep Medicine Clinics, 2017, 12, (1), p, 73-85.
4. 4- S.W.A. Bergen, A. Antoniou, “Design of Nonrecursive Digital Filters Using the Ultraspherical Window Function”, EURASIP Journal on Applied Signal Processing, 2005, 12, 1910-1922.
5. 5- E. Torbet, M. J. Devlin,W. B. Dorwart, et al., “A measurement of the angular power spectrum of the microwave background made from the high Chilean Andes,” The Astrophysical Journal, 1999, vol. 521, pp. L79–L82.
6. 6- B. Picard, E. Anterrieu, G. Caudal, and P. Waldteufel, “Improved windowing functions for Y-shaped synthetic aperture imaging radiometers,” in Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS ’02), vol. 5, pp. 2756–2758, Toronto, Ont, Canada, June 2002.
7. 7- P. Lynch, “The Dolph-Chebyshev window: a simple optimal filter,” Monthly Weather Review, vol. 125, pp. 655–660, 1997.
8. 8- M. Dessouky, H. Sharshar, Y. Albagory, “A novel tapered beamforming window for uniform concentric circular arrays”, Journal of Electromagn. Waves and Appl, 2006, 20/4: 2077-2089.

9. 9- A. Antoniou, “Digital signal processing: signal, systems, and filters”, New York: McGraw-Hill; 2005.
10. 10- D. Ashutosh, J. Alok, CS. Pramod, “Design and performance analysis of adjustable window functions based cosine modulated filter banks”, Digital Signal Processing, 23/1: 412-417, 2013.
11. 11- C.L. Dolph, “A current distribution for broadside arrays which optimizes the relationship between beamwidth and side-lobe level”, Proc. IRE, June, 34, 335-348, 1946.
12. 12- J.F. Kaiser, “Nonrecursive digital filter design using I0-sinh window function”, Proc. IEEE Int. Symp. Circuits and Systems, San Francisco, Calif., USA, 20-23 April, p. 20-23, 1974.
13. 13- J.F. Kaiser, R.W. Schafer, “On the use of the I0-sinh window for spectrum analysis”, 1980, IEEE Trans. Acoustics, Speech, and Signal Processing, 28/1, 105- 107.
14. 14- T. Saramaki, “Finite impulse response filter design”, in Handbook for Digital Signal Processing, S.K. Mitra and J.F. Kaiser, Eds. Wiley & Sons, New York, NY, US, 1993.
15. 15- K. Avci, A. Nacaroğlu, “Cosh window family and its application to FIR filter design”, International Journal of Electronics and Communications-AEU, 2009, 63 906-917.
16. 16- E. Avaroğlu., T. Tuncer, A.B. Özer, B. Ergen, M. Türk, A novel chaos-based post-processing for TRNG. Nonlinear Dyn., 2015, 1–11.
17. 17- E. Avaroğlu., T. Tuncer, A.B. Özer, M. Türk, “A new method for hybrid pseudo random number generator”. J. Microelectron. Electron. Compon, 2014, 4(4), 303–311.
18. 18- C. Dominguez, H. Hassan, A. Crespo, “Emotional robot control architecture implementation using FPGAs”, Journal of Systems Architecture, 2017, 72, 29–41.
19. 19- M.G. Egila, M.A. El-Moursy, A. E.El-Hennawy, H.A. El-Simary, A. Zaki, “FPGA-based electrocardiography (ECG) signal analysis systemusing least-square linear phase finite impulse response (FIR) filter”, Journal of Electrical Systems and Information Technology, 3, 513–526, 2016.
20. 20- R. Lehto, T. Taurén, O.Vainio, “ Recursive FIR filter structures on FPGA”, Microprocessors and Microsystems, 2011, 35, 595–602.
21. 21- EL Menshawy, M., Benharref, A., Serhani, M., An automatic mobile-health based approach for EEG epileptic seizures detection, Expert Systems with Applications, 2015, 42, (20), p, 7157-7174.
22. 22- Khairul Azami Sidek, Vu Mai, Ibrahim Khalil, Data mining in mobile ECG based biometric identification, J. Netw. Comput. Appl. 2014, 44,83–91.
23. 23- Serhani M.A., M. El Menshawy, A. Benharref, S. Harous, A.N. Navaz, New algo- rithms for processing time-series big EEG data within mobile health monitoring systems, Comput. Methods Programs Biomed., 2017, 149, 79–94.
24. 24- Tuncer, S. A., Alkan, A., Segmentation of thyroid nodules with K-means algorithm on mobile devices, 2015 16th IEEE International Symposium on Computational Intelligence and Informatics (CINTI), Budapest, pp. 345-348, 2015.
25. 25- Sárkány, N., Tihanyi, A., Szolgay, P., The design of a mobile multi-channel bio-signal measuring system for rehabilitation purposes, 14th International Workshop on Cellular Nanoscale Networks and their Applications (CNNA), 2014.