Low-energy formulations of support vector machine kernel functions for biomedical sensor applications

Journal of Signal Processing Systems

Volumn 69, Issue 3, 2012, Pages 339-349

  Lee, Kyong Ho ^a   Kung, Sun Yuan ^a   Verma, Naveen ^a  

^a PRINCETON UNIVERSITY   (United States)

Author keywords

Biomedical devices; Energy efficiency; Kernel energy trade off; Machine learning

Indexed keywords

ACCURATE ANALYSIS; ARRHYTHMIA DETECTION; BIOMEDICAL APPLICATIONS; BIOMEDICAL DEVICES; BIOMEDICAL SENSORS; KERNEL FUNCTION; KERNEL-ENERGY TRADE-OFF; LOW ENERGIES; LOW POWER; LOW-POWER DEVICES; MACHINE-LEARNING; PATIENT DATA; POLYNOMIAL TRANSFORMATIONS; RADIAL BASIS FUNCTIONS; REAL-TIME COMPUTATIONS; SEIZURE DETECTION;

BIOSENSORS; DISEASES; ENERGY EFFICIENCY; HOSPITAL DATA PROCESSING; LEARNING SYSTEMS; MEDICAL APPLICATIONS; SIGNAL PROCESSING;

DETECTORS;

EID: 84866051903     PISSN: 19398018     EISSN: 19398115     Source Type: Journal    
DOI: 10.1007/s11265-012-0672-8     Document Type: Article

References (24)

1. Yazicioglu, R. F., et al. (2007). A 60 μW60 nV/√ Hz readout front-end for portable biopotential acquisition systems. IEEE Journal of Solid-State Circuits, 42(5), 1100-1110. (Pubitemid 46645027)
2. Verma, N., et al. (2010). A micro-power EEG acquisition SoC with integrated feature extraction processor for a chronic seizure detection system. IEEE Journal of Solid-State Circuits, 45, (4), 804-816.
3. Denison, T., et al. (2007). 2 μW 100 nV/√ Hz chopperstabilized instrumentation amplifier for chronicmeasurement of neural field potentials. IEEE Journal of Solid-State Circuits, 42(12), 2934-2945.
4. Verma, N., et al. (2011). Data-driven approaches for computation in intelligent biomedical devices: a case study of EEG monitoring for chronic seizure detection. Journal of Low Power Electronics and Applications, 1(1), 150-174.
5. Schachter, S. C. (1998). Vagus nerve stimulator. Epilepsia, 39, 677-686.
6. Csavoy, A., et al. (2009). Creating support circuits for the nervous system: considerations for "brain-machine" interfacing. In VLSI circuits, 2009 symposium on (pp. 4-7).
7. Dishman, E. (2004). Inventing wellness systems for aging in place. IEEE Computer, 37, 34-41.
8. Shoaib, M., et al. (2011). A low-energy computation platform for data-driven biomedical monitoring algorithms.DAC (pp. 591-596).
9. Hau, D., & Coiera, E. (1994). Learning qualitative models from physiological signals (Vol. SS-94-01, pp. 67-71). AAAI Technical Report, Menlo Park, CA, U.S.A., AAAI Press.
10. Lucas, P. (2004). Bayesian analysis, pattern analysis and data mining in health care. Current Opinion in Critical Care, 10(5), 399-403. (Pubitemid 39313512)
11. Chandrakasan, A., et al. (2008). Ultralow-power electronics for biomedical applications. Annual Review of Biomedical Engineering, 10, 247-274.
12. Vapnik, V. N. (1995). The nature of statistical learning theory. New York: Springer.
13. Christianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge, U.K.: Cambridge University Press.
14. Hsu, C. W., & Lin, C. J. (2002). A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks, 13, 415-425.
15. de Chazal, P., et al. (2004). Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Transactions on Biomedical Engineering, 51(7), 1196-1206. (Pubitemid 38787925)
16. Shoeb, A., & Guttag, J.(2010). Application of machine learning to epileptic seizure detection. In Proc. of int. conf. on machine learning.
17. Eriksson, J., & Finne, N. MSPsim. Swedish Institute of Computer Science. http://www.sics.se/project/mspsim.
18. Shih, E., & Guttag, J. (2008). Reducing energy consumption of multi-channel mobile medical monitoring algorithms. In Proceedings of the second international workshop on systems and networking support for healthcare and assisted living environments.
19. Glassman, E., & Guttag, J. (2006). Reducing the number of channels for an ambulatory patient-specific EEG-based epileptic seizure detector by applying recursive feature elimination. In Proc. of the 28th IEEE EMBS annual international conference (pp. 2175-2178). (Pubitemid 46520332)
20. Kung, S. Y., et al. (2010). Feature selection for genomic signal processing: Unsupervised, supervised, and self-supervised scenarios. Journal of Signal Processing Systems, 61(1), 3-20.
21. Shoeb, A., et al. (2009). A micropower support vector machine based seizure detection architecture for embedded medical devices. In Proc. IEEE eng. med. biol. soc. conf. (pp. 4202-4205).
22. Shoeb, A. (2009). Application of machine learning to epileptic seizure onset detection and treatment. Ph.D. Thesis, MIT.
23. Joachims, T. SVM Light. University of Dortmund. http://www.cs.cornell. edu/People/tj/svm-light.
24. Goldberger, A. L., et al. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiological signals. Circulation, 101(23), e215-e220.