Intelligent biomedical devices implies systems that are able to detect specific physiological processes in patients so that particular responses can be generated. This closed-loop capability can have enormous clinical value when we consider the unprecedented modalities that are beginning to emerge for sensing and stimulating patient physiology. Both delivering therapy (e.g., deep-brain stimulation, vagus nerve stimulation, etc.) and treating impairments (e.g., neural prosthesis) requires computational devices that can make clinically relevant inferences, especially using minimally-intrusive patient signals. The key to such devices is algorithms that are based on data-driven signal modeling as well as hardware structures that are specialized to these. This paper discusses the primary application-domain challenges that must be overcome and analyzes the most promising methods for this that are emerging. We then look at how these methods are being incorporated in ultra-low-energy computational platforms and systems. The case study for this is a seizure-detection SoC that includes instrumentation and computation blocks in support of a system that exploits patient-specific modeling to achieve accurate performance for chronic detection. The SoC samples each EEG channel at a rate of 600 Hz and performs processing to derive signal features on every two second epoch, consuming 9 μJ/epoch/channel. Signal feature extraction reduces the data rate by a factor of over 40×, permitting wireless communication from the patient’s head while reducing the total power on the head by 14×.
References
[1]
Benabid, A.L. Deep brain stimulation for Parkinson's disease. Curr. Opin. Neurobiol. 2003, 13, 696–706.
Kim, D.H.; Viventi, J.; Amsden, J.; Xiao, J.; Vigeland, L.; Kim, Y.S.; Blanco, J.; Frechette, E.; Contreras, D.; Kaplan, D.; et al. Dissolvable films of silk fibroin for ultrathin, conformal bio-integrated electronics. Nat. Mater. 2010, 9, 511–517.
[4]
Graudejus, O.; Yu, Z.; Jones, J.; Morrison, B.; Wagner, S. Characterization of an elastically stretchable microelectrode array and its application to neural field potential recordings. J. Electrochem. Soc. 2009, 156, 85–94.
[5]
Viventi, J.; Kim, D.H.; Moss, J.; Kim, Y.S.; Blanco, J.; Anetta, N.; Hicks, A.; Xiao, J.; Huang, Y.; Callans, D.J.; et al. Dissolvable films of silk fibroin for ultrathin, conformal bio-integrated electronics. Sci. Trans. Med. 2010, 2, 2694–2699.
[6]
Chandrakasan, A.; Verma, N.; Daly, D. Ultralow-power electronics for biomedical applications. Annu. Rev. Biomed. Eng. 2008, 10, 247–274.
[7]
Leong, T.; Aronsky, D.; Michael Shabot, M. Guest editorial: Computer-based decision support for critical and emergency care. J. Biomed. Inform. 2008, 41, 409–412.
[8]
Wallis, L. Alarm fatigue linked to patient's death. Am. J. Nurs. 2010, 110, 16.
[9]
Dishman, E. Inventing wellness systems for aging in place. IEEE Comput. 2004, 37, 34–41.
[10]
Tsien, C. TrendFinder: Automated detection of alarmable trends. Ph.D. Thesis, Massachusetts Institute of Technology, Cambridge, MA, USA, 2000.
[11]
Tompkins, W.J. Biomedical Digital Signal Processing: C Language Examples and Laboratory Experiments for the IBM PC; Prentice-Hall: Bergen County, NJ, USA, 1993.
[12]
Hau, D.; Coiera, E. Learning Qualitative Models from Physiological Signals. AAAI Technical Report SS-94-01 1994, 67–71.
[13]
Hagmann, C.F.; Robertson, N.J.; Azzopardi, D. Artifacts on electroencephalograms may influence the amplitude-integrated EEG classification: A qualitative analysis in neonatal encephalopathy. Pediatrics 2006, 118, 2552–2554.
[14]
Yazicioglu, R. Biopotential Readout Circuits for Portable Acquisition Systems. Ph.D. Thesis, Katholieke Universiteit, Leuven, Belgium, 2008.
[15]
Shoeb, A.; Schachter, S.; Schomer, D.; Bourgeois, B.; Treves, S.T.; Guttag, J. Detecting seizure onset in the ambulatory setting: Demonstrating feasibility. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2005, 4, 3546–3550.
[16]
Gotman, J.; Ives, J.; Gloor, G. Frequency content of eeg and emg at seizure onset: Possibility of removal of EMG artefact by digital filtering. EEG Clin. Neurophysiol. 1981, 52, 626–639.
[17]
Shoeb, A.; Bourgeois, B.; Treves, S.; Schachter, S.; Guttag, J. Impact of patient-specificity on seizure onset detection performance. Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society, Lyon, France, 2007; pp. 4110–4114.
[18]
Meyfroidt, G.; Guiza, F.; Ramon, J.; Bruynooghe, M. Machine learning techniques to examine large patient databases. Best Pract. Res. Clin. Anaesthesiol. 2009, 23, 127–143.
[19]
Sajda, P. Machine learning for diagnosis and detection of disease. Annu. Rev. Biomed. Eng. 2006, 8, 537–565.
[20]
Podgorelec, V.; Druzovec, T.W. Some applications of intelligent systems in medicine. Proceedings of the IEEE 3rd International Conference on Computational Cybernetics, Mauritius, 2005; pp. 35–39.
[21]
Shoeb, A.; Guttag, J. Application of Machine Learning to Epileptic Seizure Detection. Proceedings of the International Conference on Machine Learning, Haifa, Israel, 2010.
[22]
de Chazal, P.; O'Dwyer, M.; Reilly, R.B. Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 2004, 51, 1196–1206.
[23]
Sorenson, D.; Grissom, C.; Carpenter, L.; Austin, A.; Sward, K.; Napoli, L.; Warner, H.; Morris, A. and for the Reengineering Clinical Research in Critical Care Investigators. A frame-based representation for a bedside ventilator weaning protocol. J. Biomed. Inform. 2008, 41, 461–468.
[24]
Jouny, C.; Bergey, G. Dynamics of Epileptic Seizures During Evolution And Propagation. In Computational Neuroscience in Epilepsy, 1st ed.; Soltesz, I., Staley, K., Eds.; Academic Press: San Diego, CA, USA, 2008; Volume 28, pp. 457–470.
[25]
Hsu, C.W.; Lin, C.J. A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Networks 2002, 13, 415–425.
[26]
Rubinstein, Y.D.; Hastie, T. Discriminative vs Informative Learning. Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining; AAAI Press: Menlo Park, CA, USA, 1997; pp. 49–53.
[27]
Ng, A.Y.; Jordan, M.I. On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes. Adv. Neural Inf. Process. Syst. 2001, 14, 841–848.
[28]
Cristianini, N.; Shawe-Taylor, J. An Introduction to Support Vector Machines and Other Kernel Based Learning Methods; Cambridge University Press: Cambridge, UK, 2000.
[29]
If the classes cannot be well separated by a hyper-plane, the SVM can be used to determine a quadratic or circumferential decision boundary. The SVM determines a nonlinear boundary in the input feature space by solving for linear boundary in higher-dimensional feature space, which is formed by introducing a kernel.
[30]
Theodoridis, S.; Koutroumbas, K. Linear Classifiers. In Pattern Recognition; Elsevier: Oxford, UK, 2009. Chapter 3.
[31]
Joachims, T. Text Categorization with Support-Vector Machines: Learning with Many Relevant Features. Proceedings of the European Conference on Machine Learning; Springer: Berlin, Germany, 1998; pp. 137–142.
[32]
Akbani, R.; Kwek, S.; Japkowicz, N. Applying Support-Vector Machines to Imbalanced Datasets. Proceedings of the European Conference on Machine Learning; Springer: Berlin, Germany, 2004.
[33]
Kay, S.M. Cramer-Rao Lower Bound. In Fundamentals of Statistical Signal Processing: Detection Theory; Prentice-Hall: Bergen County, NJ, USA, 1998. Chapter 3; pp. 60–92.
[34]
Bishop, C.M. Probability Distributions. In Pattern Recognition and Machine Learning; Springer: Berlin, Germany, 2006. Chapter 2; pp. 67–127.
[35]
Lotte, F.; Congedo, M.; Lauyer, A.; Lamarche, F.; Arnaldi, B. A Review of Classification Algorithms for EEG-Based Brain-Computer Interface. J. Neural Eng. 2007, 4, R1–R13.
[36]
Shih, E.I. Reducing the Computational Demands of Medical Monitoring Classifiers by Examining Less Data. Ph.D. Thesis, Massachu-setts Institute of Technology, Cambridge, MA, USA, 2010.
[37]
Avestruz, A.T.; Santa, W.; Carlson, D.; Jensen, R.; Stanslaski, S.; Helfenstine, A.; Denison, T. A 5 μW/channel spectral analysis IC for chronic bidirectional brain-machine interfaces. IEEE J. Solid-State Circuits 2008, 43, 3006–3024.
[38]
Yazicioglu, R.F.; Kim, S.; Torfs, T.; Merken, P.; Hoof, C.V. A 30 μW analog signal processor ASIC for biomedical signal monitoring. Proceedings of the IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC), San Francisco, CA, USA, 2010; pp. 124–125.
[39]
Shoeb, A.; Carlson, D.; Panken, E.; Denison, T. A Micropower Support Vector Machine Based Seizure Detection Architecture for Embedded Medical Devices. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2009), Minneapolis, MN, USA, 2009; pp. 4202–4205.
[40]
Texas Instruments. Low-cost low-power 2.4 GHz RF transmitter. Available Online: http://focus.ti.com/docs/prod/folders/print/cc2550.html (accessed on 1 September 2010).
[41]
Verma, N.; Shoeb, A.; Bohorquez, J.; Dawson, J.; Guttag, J.; Chandrakasan, A.P. A micro-power EEG acquisition SoC with integrated feature extraction processor for a chronic seizure detection system. IEEE J. Solid-State Circuits 2010, 45, 804–816.
[42]
Dozio, R.; Baba, A.; Assambo, C.; Burke, M.J. Time based measurement of the impedance of the skin-electrode interface for dry electrode ECG recording. Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2007), Lyon, France, 2007; pp. 5001–5004.
[43]
Enz, C.; Temes, G. Circuit techniques for reducing the effects of op-amp imperfections: auto-zeroing, correlated double sampling, and chopper stabilization. Proc. IEEE 1996, 84, 1584–1614.
[44]
Denison, T.; Consoer, K.; Santa, W.; Avestruz, A.T.; Cooley, J.; Kelly, A. A 2 μW 100 nV/rtHz chopper-stabilized instrumentation amplifier for chronic measurement of neural field potentials. IEEE J. Solid-State Circuits 2007, 42, 2934–2945.
[45]
Yazicioglu, R.; Merken, P.; Puers, R.; Hoof, C.V. A 60 μW 60 nV / H z readout front-end for portable biopotential acquisition system. IEEE J. Solid-State Circuits 2007, 42, 1100–1110.
[46]
Verma, N.; Chandrakasan, A.P. An ultra low energy 12-bit rate-resolution scalable SAR ADC for wireless sensor nodes. IEEE J. Solid-State Circuits 2007, 42, 1196–1205.
[47]
Wang, A.; Chandrakasan, A.P. A 180 mV subthreshold FFT processor using a minimum energy design methodology. IEEE J. Solid-State Circuits 2005, 40, 310–319.
[48]
Kwong, J.; Ramadass, Y.K.; Verma, N.; Chandrakasan, A. A 65 nm Sub-Vt microcontroller with integrated SRAM and switched capacitor DC-DC converter. IEEE J. Solid-State Circuits 2009, 44, 115–126.
[49]
Verma, N. Ultra-Low-Power SRAM Design In High Variability Advanced CMOS. Ph.D. Thesis, Massachusetts Institute of Technology, Cambridge, MA, USA, 2009.
[50]
Kwong, J.; Chandrakasan, A. Variation-Driven Device Sizing for Minimum Energy Sub-threshold Circuits. Proceedings of the International Symposium on Low Power Electronics and Design, Tegernsee, Germany, 2006; pp. 8–13.
