Abstract
Highly miniaturised, wearable computing and communication systems allow unobtrusive, convenient and long term monitoring of a range of physiological parameters. For long term operation from the physically smallest batteries, the average power consumption of a wearable device must be very low. It is well known that the overall power consumption of these devices can be reduced by the inclusion of low power consumption, real-time compression of the raw physiological data in the wearable device itself. Compressive sensing is a new paradigm for providing data compression: it has shown significant promise in fields such as MRI; and is potentially suitable for use in wearable computing systems as the compression process required in the wearable device has a low computational complexity. However, the practical performance very much depends on the characteristics of the signal being sensed. As such the utility of the technique cannot be extrapolated from one application to another. Long term electroencephalography (EEG) is a fundamental tool for the investigation of neurological disorders and is increasingly used in many non-medical applications, such as brain–computer interfaces. This article investigates in detail the practical performance of different implementations of the compressive sensing theory when applied to scalp EEG signals.
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Abdulghani AM, Casson AJ, Rodriguez-Villegas E (2009) Quantifying the feasibility of compressive sensing in portable electroencephalography systems. In: Schmorrow DD, Estabrooke IV, Grootjen M (eds) 5th international conference on foundations of augmented cognition, LNCS 5638. Springer, Berlin/Heidelberg, pp 319–328
Abdulghani AM, Rodriguez-Villegas E (2010) Compressive sensing: from “compressing while sampling” to “compressing and securing while sampling”. In: Proceedings of the 32nd international conference of the IEEE engineering in medicine and biology society, Buenos Aires, IEEE, Piscataway, pp 1127–1130
Abdulghani AM, Casson AJ, Rodriguez-Villegas E (2010) Quantifying the performance of compressive sensing on scalp EEG signals. In: Proceedings of the 3rd international symposium on applied sciences in biomedical and communication technologies, Rome, IEEE, Piscataway, pp 1–5
Andrle M, Rebollo-Neira L (2005) Cardinal B-spline dictionaries on a compact interval. Appl Comput Harmon Anal 18:336–346
Andrle M, Rebollo-Neira L (2005) Spline wavelet dictionaries for non-linear signal approximation. In: Proceedings of the international conference on interactions between wavelets and splines, Athens
Andrle M, Rebollo-Neira L (2008) From cardinal spline wavelet bases to highly coherent dictionaries. J Phys A 41:172001
Antoniol G, Tonella P (1997) EEG data compression techniques. IEEE Trans Biomed Eng 44:105–114
Aviyente S (2007) Compressed sensing framework for EEG compression. In: Proceedings of the 14th IEEE/SP workshop on signal processing, Madison, IEEE, Piscataway, pp 181–184
Candes EJ, Tao T (2005) Decoding by linear programming. IEEE Trans Inform Theor 51:4203–4215
Candes EJ (2006) Compressive sampling. In: Proceedings of the international congress of mathematicians, Madrid, pp 1433–14
Candes EJ, Wakin MB (2008) An introduction to compressive sampling. IEEE Signal Process Mag 25:21–30
Cardenas-Barrera J, Lorenzo-Ginori J, Rodriguez-Valdivia E (2004) A wavelet-packets based algorithm for EEG signal compression. Med Inform Internet Med 29:15–27
Carron I (2010) CS: Q&A with Esther Rodriguez-Villegas on a compressive sensing EEG. http://nuit-blanche.blogspot.com/ Accessed 30 Jan 2010
Casson AJ, Rodriguez-Villegas E (2009) Toward online data reduction for portable electroencephalography systems in epilepsy. IEEE Trans Biomed Eng 56:2816–2825
Casson AJ, Yates DC, Smith SJ, Duncan JS, Rodriguez-Villegas E (2010) Wearable electroencephalography. IEEE Eng Med Biol Mag 29:44–56
Chen F, Chandrakasan AP, Stojanovic V (2010) A signal-agnostic compressed sensing acquisition system for wireless and implantable sensors. In: Proceedings of the IEEE custom integrated circuits conference, San Jose, IEEE, Piscataway, pp 1–4
Chen SS, Donoho DL, Saunders MA (2001) Atomic decomposition by basis pursuit. SIAM Rev 43:129–159
Donoho DL (2006) Compressed sensing. IEEE Trans Inform Theor 52:1289–1306
Gamper U, Boesiger P, Kozerke S (2008) Compressed sensing in dynamic MRI. Magn Reson Med 59:365–373
Gurkan H, Guz U, Yarman BS (2009) EEG signal compression based on classified signature and envelope vector sets. Int J Circ Theor Appl 37:351–363
Hinrichs H (1991) EEG data compression with source coding techniques. J Biomed Eng 13:417–423
Latka M, Was Z, Kozik A, West BJ (2003) Wavelet analysis of epileptic spikes. Phys Rev E 67:052902
Mallat S, Zhang Z (1993) Matching pursuit in a time frequency dictionary. IEEE Trans Signal Process 41:3397–3415
Nuwer MR, Comi G, Emerson R, Fuglsang-Frederiksen A, Guerit JM, Hinrichs H, Ikeda A, Luccas FJC, Rappelsberger P (1999) IFCN standards for digital recording of clinical EEG. In: Deuschl G, Eisen A (eds) Recommendations for the practice of clinical neurophysiology: guidelines of the international federation of clinical physiology, 2nd edn. Elsevier, Amsterdam, pp 11–14
Pati YC, Rezaiifar R, Krishnaprasad PS (1993) Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In: Conference record of the 27th Asilomar conference on signals, systems and computers, Pacific Grove, IEEE, Piscataway, pp 40–44
Sieluycki C, Konig R, Matysiak A, Kus R, Ircha D, Durka PJ (2009) Single-trial evoked brain responses modeled by multivariate matching pursuit. IEEE Trans Biomed Eng 56:74–82
Sreenivas TV, Kleijn WB (2009) Compressive sensing for sparsely excited speech signals. In: Proceedings of the IEEE International conference on acoustics, speech and signal processing, Taipei, IEEE, Piscataway, pp 4125–4128
Sriraam N, Eswaran C (2008) Performance evaluation of neural network and linear predictors for near-lossless compression of EEG signals. IEEE Trans Inform Technol Biomed. 12:87–93
Verma N, Shoeb A, Bohorquez J, Dawson J, Guttag J, Chandrakasan AP (2010) A micro-power EEG acquisition SoC with integrated feature extraction processor for a chronic seizure detection system. IEEE J Solid-State Circuits 45:804–816
Wongsawat Y, Oraintara S, Tanaka T, Rao KR (2006) Lossless multi-channel EEG compression. In: Proceedings of the IEEE international symposium on circuits and systems, Kos, IEEE, Piscataway, pp 1611–1614
Zhang Y, Mei S, Chen Q, Chen Z (2008) A novel image/video coding method based on compressive sensing theory. In: Proceedings of the IEEE international conference on acoustics, speech and signal processing, Las Vagas, IEEE, Piscataway, pp 1361–1364
Acknowledgments
The research leading to these results has received funding from the European Research Council under the European Community’s 7th Framework Programme (FP7/2007-2013)/ERC Grant agreement No. 239749.
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Abdulghani, A.M., Casson, A.J. & Rodriguez-Villegas, E. Compressive sensing scalp EEG signals: implementations and practical performance. Med Biol Eng Comput 50, 1137–1145 (2012). https://doi.org/10.1007/s11517-011-0832-1
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DOI: https://doi.org/10.1007/s11517-011-0832-1