A critical care monitoring system for depth of anaesthesia analysis based on entropy analysis and physiological information database

Abstract

Diagnosis of depth of anaesthesia (DoA) plays an important role in treatment and drug usage in the operating theatre and intensive care unit. With the flourishing development of analysis methods and monitoring devices for DoA, a small amount of physiological data had been stored and shared for further researches. In this paper, a critical care monitoring (CCM) system for DoA monitoring and analysis was designed and developed, which includes two main components: a physiologic information database (PID) and a DoA analysis subsystem. The PID, including biologic data and clinical information was constructed through a browser and server model so as to provide a safe and open platform for storage, sharing and further study of clinical anaesthesia information. In the analysis of DoA, according to our previous studies on approximate entropy, sample entropy (SampEn) and multi-scale entropy (MSE), the SampEn and MSE were integrated into the subsystem for indicating the state of patients underwent surgeries in real time because of their stability. Therefore, this CCM system not only supplies the original biological data and information collected from the operating room, but also shares our studies for improvement and innovation in the research of DoA.

Design and Evaluation of a Real Time Physiological Signals Acquisition System Implemented in Multi-Operating Rooms for Anesthesia

“…Anesthesia) without any dependence and interference on HIS. From the statistics, it is clear that the proposed system collection rate is much higher than the manual one [23], which speeds the database construction much more. Results prove the capability of data transmission and coexistence with other wireless network.…”

“…Besides, the advanced deep neural network applied in data analysis keeps an uprising trend, which might improve our accuracy rate of DoA evaluation [47,48]. In addition, another significant point is that current system only focuses on the data collection instead of real-time processing simultaneously although a standalone real-time processing demo system has been previously developed in our group [23]. Front-end user friendly interface based on the wireless system should utilize the aforementioned advanced algorithms including section 3.3 to discover new physiological patterns during surgery.…”

“…Fig. 3 shows the structure of original collection framework [23]. The procedure is quite complex and time consuming.…”

Sample Entropy Analysis of EEG Signals via Artificial Neural Networks to Model Patients’ Consciousness Level Based on Anesthesiologists Experience

“…In previous study [ 27 , 28 ], SampEn has been proved better than ApEn. Moreover, these two algorithms are proposed to monitor the DOA of patients during surgeries, which show that the SampEn is more adaptive to the real time detection and has better ability in accessing the level of consciousness of patients during surgery [ 29 – 31 ]. The only drawback of SampEn is the calculated entropy value which is ranged from 0 to 3 variously.…”

Sample Entropy in Electrocardiogram During Atrial Fibrillation

“…16 For example, SampEn for EEG was proposed to monitor the depth of anesthesia for patients during surgery, and SampEn has been found to be more feasible for real-time detection. 17 In this study, we analyzed SampEn to investigate the irregularity of ECG signals for AF. Additionally, we developed a new method for plotting ECG signals with time-delayed coordinates.…”