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Low-energy Formulations of Support Vector Machine Kernel Functions for Biomedical Sensor Applications

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Abstract

Although physiologically-indicative signals can be acquired in low-power biomedical sensors, their accurate analysis imposes several challenges. Data-driven techniques, based on supervised machine-learning methods provide powerful capabilities for potentially overcoming these, but the computational energy is typically too severe for low-power devices. We present a formulation for the kernel function of a support-vector machine classifier that can substantially reduce the real-time computations involved. The formulation applies to kernel functions employing polynomial transformations. Using two representative biomedical applications (EEG-based seizure detection and ECG-based arrhythmia detection) employing clinical patient data, we show that the polynomial transformation yields accuracy performance comparable to the most powerful available transformation (i.e., the radial-basis function), and the proposed formulation reduces the energy by over 2500× in the arrhythmia detector and 9.3-198× in the seizure detector (depending on the patient).

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Authors and Affiliations

  1. Princeton University, Engineering Quadrangle, Olden Street, Princeton, NJ, 08544, USA

    Kyong Ho Lee, Sun-Yuan Kung & Naveen Verma

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  1. Kyong Ho Lee
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  2. Sun-Yuan Kung
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  3. Naveen Verma
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Correspondence to Kyong Ho Lee.

Additional information

The authors thank Dr. A. Shoeb (MGH, MIT, now with WeatherBill) for valuable discussions and algorithm testing support. They also acknowledge the support of the Gigascale Systems Research Center, one of six research centers funded under the Focus Center Research Program (FCRP), a Semiconductor Research Corporation entity.

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Lee, K.H., Kung, SY. & Verma, N. Low-energy Formulations of Support Vector Machine Kernel Functions for Biomedical Sensor Applications. J Sign Process Syst 69, 339–349 (2012). https://doi.org/10.1007/s11265-012-0672-8

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  • DOI: https://doi.org/10.1007/s11265-012-0672-8

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