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

Although physiologically-indicative signals can be acquired in low-power biomedical sensors, their accurate analysis imposes several challenges. Data-driven techniques, based on supervised machinelearning 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 radialbasis 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).