The use of wireless body sensor networks is gaining popularity in monitoring and communicating information about a person’s health. In such applications, the amount of data transmitted by the sensor node should be minimized. This is because the energy available in these battery powered sensors is limited. In this paper, we study the wireless transmission of electroencephalogram (EEG) signals. We propose the use of a compressed sensing (CS) framework to efficiently compress these signals at the sensor node. Our framework exploits both the temporal correlation within EEG signals and the spatial correlations amongst the EEG channels. We show that our framework is up to eight times more energy efficient than the typical wavelet compression method in terms of compression and encoding computations and wireless transmission. We also show that for a fixed compression ratio, our method achieves a better reconstruction quality than the CS-based state-of-the art method. We finally demonstrate that our method is robust to measurement noise and to packet loss and that it is applicable to a wide range of EEG signal types.
References
[1]
OECD. OECD Health Data 2012—Frequently Requested Data. Available online: http://www.oecd.org/health/healthdata (accessed on 28 August 2013).
[2]
Hagist, C.; Kotlikoff, L. Who's Going Broke? Comparing Growth in Healthcare Costs in Ten OECD Countries. Technical Report; National Bureau of Economic Research: Cambridge, MA, USA, 2005.
[3]
Milenkovi？, A.; Otto, C.; Jovanov, E. Wireless sensor networks for personal health monitoring: Issues and an implementation. Comput. Commun. 2006, 29, 2521–2533.
[4]
Mohseni, H.R.; Maghsoudi, A.; Shamsollahi, M.B. Seizure Detection in EEG Signals: A Comparison of Different Aapproaches. Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '06), New York, NY, USA, 30 August–3 September 2006; Volume Supplement, pp. 6724–6727.
[5]
Blankertz, B.; Dornhege, G.; Krauledat, M.; Muller, K.R.; Kunzmann, V.; Losch, F.; Curio, G. The Berlin Brain-Computer Interface: EEG-based communication without subject training. IEEE Trans. Neural Syst. Rehabil. Eng. 2006, 14, 147–152.
[6]
Bashashati, A.; Fatourechi, M.; Ward, R.K.; Birch, G.E. A survey of signal processing algorithms in brain-computer interfaces based on electrical brain signals. J. Neural Eng. 2007, 4, R32–R57.
[7]
Feinberg, I.; Koresko, R.L.; Heller, N. EEG sleep patterns as a function of normal and pathological aging in man. J. Psychiatr. Res. 1967, 5, 107–144.
[8]
Jordan, K.G. Emergency EEG and continuous EEG monitoring in acute ischemic stroke. J. Clin. Neurophysiol. 2004, 21, 341–352.
[9]
Upton, A.; Gumpert, J. Electroencephalography in diagnosis of herpes-simplex encephalitis. Lancet 1970, 295, 650–652.
[10]
Jeong, J. EEG dynamics in patients with Alzheimer's disease. Clin. Neurophysiol. 2004, 115, 1490–1505.
[11]
Mamaghanian, H.; Khaled, N.; Atienza, D.; Vandergheynst, P. Compressed sensing for real-time energy-efficient ECG compression on wireless body sensor nodes. IEEE Trans. Biomed. Eng. 2011, 58, 2456–2466.
[12]
Candes, E.J.; Wakin, M.B. An introduction to compressive sampling. IEEE Signal Process. Mag. 2008, 25, 21–30.
[13]
Aviyente, S. Compressed Sensing Framework for EEG Compression. Proceedings of the IEEE/SP 14th Workshop on Statistical Signal Processing (SSP '07), Madison, WI, USA, 26–29 August 2007; pp. 181–184.
Abdulghani, A.M.; Casson, A.J.; Rodriguez-Villegas, E. Quantifying the Performance of Compressive Sensing on Scalp EEG Signals. Proceedings of the 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL), Rome, Italy, 7–10 November 2010; pp. 1–5.
[16]
Mijovic, B.; Matic, V.; de Vos, M.; van Huffel, S. Independent Component Analysis as a Preprocessing Step for data Compression of Neonatal EEG. Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '11), Boston, MA, USA, 30 August 2011; pp. 7316–7319.
[17]
Fauvel, S.; Agarwal, A.; Ward, R. Compressed Sensing and Energy-Aware Independent Component Analysis for Compression of EEG Signals. Proceedings of the 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP '13), Vancouver, BC, Canada, 26–31 May 2013. in press.
[18]
Chen, F.; Chandrakasan, A.; Stojanovic, V. Design and analysis of a hardware-efficient compressed sensing architecture for data compression in wireless sensors. IEEE J. Solid-State Circuits 2012, 47, 744–756.
[19]
Candes, E.J.; Tao, T. Decoding by linear programming. IEEE Trans. Inf. Theory 2005, 51, 4203–4215.
Gilbert, A.; Indyk, P. Sparse recovery ysing sparse matrices. In IEEE Proc.; 2010; Volume 98, pp. 937–947.
[22]
Zhang, Z.; Jung, T.; Makeig, S.; Rao, B. Compressed sensing of EEG for wireless telemonitoring with low energy consumption and inexpensive hardware. IEEE Trans. Biomed. Eng. 2013, 60, 221–224.
[23]
Gabor, D. Theory of communication. Part 1: The analysis of information. J. Inst. Electr. Eng. Part III Radio Commun. Eng. 1946, 93, 429–441.
[24]
Barwiński, M. Product-Based Metric for Gabor Functions and Its Implications for the Matching Pursuit Analysis. Ms.C. Thesis, University of Warsaw, Warsaw, Poland, 2004.
[25]
Van den Berg, E.; Friedlander, M.P. Probing the Pareto frontier for basis pursuit solutions. SIAM J. Sci. Comput. 2009, 31, 890–912.
[26]
Higgins, G.; McGinley, B.; Glavin, M.; Jones, E. Low Power Compression of EEG Signals Using JPEG2000. Proceedings of IEEE the 4th International Conference on Pervasive Computing Technologies for Healthcare, Munich, Germany, 22–25 March 2010; pp. 1–4.
[27]
Zhang, Z.; Rao, B.D. Extension of SBL algorithms for the recovery of block sparse signals with intra-block correlation. Signal Process. 2013, 61, 2009–2015.
[28]
Blankertz, B.; Dornhege, G.; Krauledat, M.; Mller, K.; Curio, G. The non-invasive Berlin Brain-Computer Interface: Fast acquisition of effective performance in untrained subjects. NeuroImage 2007, 37, 539–550.
[29]
Goldberger, A.L.; Amaral, L.A.N.; Glass, L.; Hausdorff, J.M.; Ivanov, P.C.; Mark, R.G.; Mietus, J.E.; Moody, G.B.; Peng, C.K.; Stanley, H.E.; PhysioBank, PhysioToolkit. PhysioNet: Components of a new research resource for complex physiologic signals. Circulation 2000, 101, e215–e220.
[30]
Shoeb, A.H. Application of Machine Learning to Epileptic Seizure Onset Detection and Treatment. Ph.D. Thesis, Massachusetts Institute of Technology, Cambridge, MA, USA, 2009.
[31]
Terzano, M.G.; Parrino, L.; Sherieri, A.; Chervin, R.; Chokroverty, S.; Guilleminault, C.; Hirshkowitz, M.; Mahowald, M.; Moldofsky, H.; Rosa, A.; et al. Atlas, rules, and recording techniques for the scoring of cyclic alternating pattern (CAP) in human sleep. Sleep Med. 2001, 2, 537–553.
[32]
Yazicioglu, R.; Torfs, T.; Merken, P.; Penders, J.; Leonov, V.; Puers, V.; Gyselinckx, B.; van Hoof, C. Ultra-low-power biopotential interfaces and their applications in wearable and implantable systems. Microelectron. J. 2009, 40, 1313–1321.
[33]
Titzer, B.L.; Lee, D.K.; Palsberg, J. Avrora: Scalable Sensor Network Simulation with Precise Timing. Proceedings of the Fourth International Symposium on Information Processing in Sensor Networks (IPSN '05), Los Angeles, CA, USA, 25 April 2005; pp. 477–482.
[34]
Gunturk, C.; Lammers, M.; Powell, A.; Saab, R.; Yilmaz, O. Sigma Delta Quantization for Compressed Sensing. Proceedings of the 44th Annual Conference on Information Sciences and Systems (CISS '10), Princeton, NJ, USA, 17–19 March 2010; pp. 1–6.
