Improving kernel-energy trade-offs for machine learning in implantable and wearable biomedical applications

Emerging biomedical sensors and stimulators offer unprecedented modalities for delivering therapy and acquiring physiological signals (e.g., deep brain stimulators). Exploiting these in intelligent, closed loop systems requires detecting specific physiological states using very low power (i.e., 1-10 mW for wearable devices, 10-100 μW for implantable devices). Machine learning is a powerful tool for modeling correlations in physiological signals, but model complexity in typical biomedical applications makes detection too computationally intensive. We analyze the computational energy trade-offs and propose a method of restructuring the computations to yield more favorable trade-offs, especially for typical biomedical applications. We thus develop a methodology for implementing low-energy classification kernels and demonstrate energy reduction in practical biomedical systems. Two applications, arrhythmia detection using electrocardiographs (ECG) from the MIT-BIH database and seizure detection using electroencephalographs (EEG) from the CHB-MIT database, are used. The proposed computational restructuring can be used with very little performance degradation, and it reduces energy by 2627x and 7.0-36.3x (depending on the patient), respectively.