An Analog Nearest Class with Multiple Centroids Classifier Implementation, for Depth of Anesthesia Monitoring
Vassilis Alimisis, Vassilis Mouzakis, Georgios Gennis, Errikos Tsouvalas, Paul P. Sotiriadis
Abstract
Monitoring the Depth of Anesthesia on a patient is crucial to maintain a safe sedation state during a surgical operation. A high dosage can directly affect the patient's health, while a low one may disrupt the operation and, in turn, lead to unavoidable damage. To this end, this work proposes a novel, low power <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(1.7\mu W)$</tex> , low voltage (0.6V) analog architecture of a Nearest Class with Multiple Centroids classifier for depth of anesthesia monitoring. The architecture consists of a bell-shaped function circuit and the Lazzaro argmax operator circuit. To verify the proper operation of the proposed classifier a real-world depth of Anesthesia dataset is utilized. Post-layout simulation results were compared with software-based ones to confirm the high accuracy of the proposed design. The implemented architecture was realized and simulated in a TSMC 90nm CMOS process, using the Cadence IC Suite.