title: A data-driven modeling approach to stochastic computation for low-energy biomedical devices.
abstract: Low-power devices that can detect clinically relevant correlations in physiologically-complex patient signals can enable systems capable of closed-loop response (e.g., controlled actuation of therapeutic stimulators, continuous recording of disease states, etc.). In ultra-low-power platforms, however, hardware error sources are becoming increasingly limiting. In this paper, we present how data-driven methods, which allow us to accurately model physiological signals, also allow us to effectively model and overcome prominent hardware error sources with nearly no additional overhead. Two applications, EEG-based seizure detection and ECG-based arrhythmia-beat classification, are synthesized to a logic-gate implementation, and two prominent error sources are introduced: (1) SRAM bit-cell errors and (2) logic-gate switching errors ('stuck-at' faults). Using patient data from the CHB-MIT and MIT-BIH databases, performance similar to error-free hardware is achieved even for very high fault rates (up to 0.5 for SRAMs and 7 × 10(-2) for logic) that cause computational bit error rates as high as 50%.
authors.affiliation: Princeton University, Princeton, NJ 08540, USA. kyonglee@princeton.edu
authors.forename: Kyong Ho
authors.initials: KH
authors.lastname: Lee
authors.forename: Kuk Jin
authors.initials: KJ
authors.lastname: Jang
authors.forename: Ali
authors.initials: A
authors.lastname: Shoeb
authors.forename: Naveen
authors.initials: N
authors.lastname: Verma
mesh_terms: D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis*; D003627:Data Interpretation, Statistical; D011211:Electric Power Supplies; D004562:Electrocardiography / Q000295:instrumentation*; D004569:Electroencephalography / Q000379:methods*; D004868:Equipment Failure; D006801:Humans; D015203:Reproducibility of Results; D012640:Seizures / Q000175:diagnosis*; D012680:Sensitivity and Specificity; D013269:Stochastic Processes