An Analog Nearest Class with Multiple Centroids Classifier Implementation, for Depth of Anesthesia Monitoring

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 $(1.7\mu W)$, 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.