In this paper, we present the development and the validation of a novel index of nociception/anti-nociception (N/AN) based on skin impedance measurement in time and frequency domain with our prototype AnspecPro device. The primary objective of the study was to compare the Anspec-PRO device with two other commercial devices (Medasense, Medstorm). This comparison was designed to be conducted under the same conditions for the three devices. This was carried out during total intravenous anesthesia (TIVA) by investigating its outcomes related to noxious stimulus. In a carefully designed clinical protocol during general anesthesia from induction until emergence, we extract data for estimating individualized causal dynamic models between drug infusion and their monitored effect variables. Specifically, these are Propofol hypnotic drug to Bispectral index of hypnosis level and Remifentanil opioid drug to each of the three aforementioned devices. When compared, statistical analysis of the regions before and during the standardized stimulus shows consistent difference between regions for all devices and for all indices. These results suggest that the proposed methodology for data extraction and processing for AnspecPro delivers the same information as the two commercial devices.

This letter presents an extension to system theory as a novel approach to provide models from clinical data under large uncertainty and poor identifiability conditions. These difficult conditions are often present in medical systems due to ethical, safety and regulatory limitations regarding application of persistent drug-related excitation to human body. Furthermore, drug-dose effect relationship is of particular challenge due to large inter-and intra-patient variability. This is strengthened by the lack of suitable instrumentation to measure the necessary information, rather making available inferences and surrogate metrics. A notable advantage of the proposed approach is its robustness to uncertainty. The efficacy of our approach was examined in clinical data from patients monitored during induction phase of target controlled intravenous anesthesia. The proposed method delivered models with physiological explainable parameters and suitable for closed loop control of anesthesia.