A Resource-Optimized Patient-Specific Nonlinear-SVM Hypertension Detection Algorithm for Minimally-Invasive High Blood Pressure Control

摘要

Design, VLSI implementation, and validation results of a patient-specific RBF-SVM algorithm for monitoring and closed-loop control of high blood pressure in patients with resistant hypertension are presented. To ensure minimal invasiveness, the algorithm only uses a single-channel ECG signal as its sensory input. The feature extraction and classification are designed and optimized to be inexpensive both in terms of computational resources and energy consumption, enabling algorithm's integration within the highly-restricted size and power budget of an implantable device. The VLSI implementation using a hardware description language is also presented. Our results show that the implementation of the algorithm on a miniature Microsemi AGL250 low-power FPGA requires 493 logic elements, 7.4kbit of memory, consumes 19.98μW dynamic power (clocked at 1MHz), and yields a classification latency of 180μs. The algorithm classification performance is evaluated on a two different pre-recorded labeled ECG database with 14 healthy and 14 sick subjects and shows an average sensitivity, specificity, and accuracy of 89%, 98%, and 94.5%, respectively.