Mohammed Shoaib
ABSTRACT
A key challenge in closed-loop chronic biomedical systems is the ability to detect complex physiological states from patient signals within a constrained power budget. Data-driven machine-learning techniques are major enablers for the modeling and interpretation of such states. Their computational energy, however, scales with the complexity of the required models. In this paper, we propose a low-energy, biomedical computation platform optimized through the use of an accelerator for data-driven classification. The accelerator retains selective flexibility through hardware reconfiguration and exploits voltage scaling and parallelism to operate at a sub-threshold minimum-energy point. Using cardiac arrhythmia detection algorithms with patient data from the MIT-BIH database, classification is achieved in 2.96 μJ (at Vdd = 0.4 V), over four orders of magnitude smaller than that on a low-power general-purpose processor. The energy of feature extraction is 148 μJ while retaining flexibility for a range of possible biomarkers.
- I. S. Abu-Khater, A. Bellaouar, and M. I. Elmasry. Circuit techniques for CMOS low-power high-performance multipliers. IEEE J. Solid-State Circuits, 31(10):1535--1546, Oct. 1996.Google Scholar
Cross Ref
- A. L. Benabid. Deep brain stimulation for Parkinson's disease. Current Op. in Neurobiology, 13:696--706, Dec. 03.Google ScholarCross Ref
- A. Csavoy, G. Molnar, and T. Denison. Creating support circuits for the nervous system: Considerations for brain-machine interfacing. In Proc. Int. Symp. VLSI Circuits, pages 4--7, Jun. 2009.Google Scholar
- P. de Chazal, M. O'Dwyer, and R. B. Reilly. Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans. Biomedical Engineering, 51(7):1196--1206, Jul. 2004.Google ScholarCross Ref
- E. Dishman. Inventing wellness systems for aging in place. IEEE Computer, 37(5):34--41, 2004. Google ScholarDigital Library
- D. Hau and E. Coiera. Learning qualitative models from physiological signals. In Proc. AAAI Symp. Artificial Intelligence in Medicine, pages 67--71, 1994.Google Scholar
- A. S. Jaffe, L. Babuin, and F. S. Apple. Biomarkers in acute cardiac disease: The present and the future. J. American College of Cardiology, 48:1--11, 2006.Google ScholarCross Ref
- T. Jaochims. SVM-Light, support vector machine. http://svmlight/jaochims.org.Google Scholar
- F. M. Khan, M. G. Arnold, and W. M. Pottenger. Hardware-based support vector machine classification in logarithmic number systems. In Proc. IEEE Int. Symp. Circuits and Systems, pages 23--26, May 2005.Google ScholarCross Ref
- M. A. Lebedev and M. A. L. Nicolelis. Brain-machine interfaces: Past, present and future. Elsevier Trends in Neurosciences, 29(9):536--546, 2006.Google ScholarCross Ref
- G. Meyfroidt, F. Guiza, J. Ramon, and M. Bruynooghe. Machine learning techniques to examine large patient databases. Best Practice & Research Clinical Anaesthesiology, 23(1):127--143, Mar. 2009.Google ScholarCross Ref
- Physionet. MIT-BIH Physionet database. http://www.physionet.org/physiobank/database.Google Scholar
- S. Cadambi et al. A massively parallel FPGA-based coprocessor for support vector machines. In Proc. Int. Symp. Field Programmable Custom Computing Machines, pages 115--122, Apr. 2009. Google ScholarDigital Library
- A. Shoeb, B. Bourgeois, S. T. Treves, S. C. Schachter, and J. Guttag. Impact of patient-specificity on seizure onset detection performance. In Proc. Int. Conf. IEEE EMBS, pages 4110--4114, Aug. 2007.Google ScholarCross Ref
- A. Shoeb, D. Carlson, E. Panken, and T. Denison. A micropower support vector machine based seizure detection architecture for embedded medical devices. In Proc. IEEE Int. Conf. EMBS, pages 4202--4205, 2005.Google Scholar
- A. Shoeb and J. Guttag. Application of machine learning to seizure detection. In Proc. Conf. Machine Learning, Jun. 2010.Google Scholar
- A. H. Shoeb. Application of Machine Learning to Epileptic Seizure Onset Detection and Treatment. Electrical and Medical Engineering, Massachusetts Institute of Technology, Boson, Massachusetts, Sep. 2009.Google Scholar
- V. Sze and A. P. Chandrakasan. A 0.4-V UWB baseband processor. In Proc. IEEE Int. Symp. Low Power Electronics and Design, pages 262--267, Aug. 2007. Google ScholarDigital Library
- Tensilica Inc. The Xtensa processor. http://www.tensilica.com.Google Scholar
- E. D. Ubeyli. ECG beats classification using multiclass support vector machines with error correcting output codes. DSP, 17(3):675--684, May 2007. Google ScholarDigital Library
- N. Verma, A. Shoeb, J. Guttag, and A. Chandrakasan. A micro-power EEG acquisition SoC with integrated seizure detection processor for continuous patient monitoring. In Proc. Symp. VLSI Circuits, pages 62--63, Jun. 2009.Google Scholar
- S. A. Vitale, P. W. Wyatt, N. Checka, J. Kedzierski, and C. L. Keast. FD-SOI process technology for subthreshold-operation ultralow-power electronics. Proc. IEEE, 98(2):333--342, Feb. 2010.Google ScholarCross Ref
- A. Wang and A. P. Chandrakasan. A 180-mV subthreshold FFT processor using a minimum energy design methodology. J. Solid-State Circuits, 40(1):310--319, Jan. 2005.Google ScholarCross Ref
Index Terms
- A low-energy computation platform for data-driven biomedical monitoring algorithms
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