Monitoring the depth of anaesthesia using bispectral analysis and closed-loop controlled administration of propofol

Correlation between bispectral index value and modified Glasgow Coma Scale score in dogs with altered level of consciousness

OBJECTIVE

This study aims to identify the correlation between bispectral index (BIS) value and modified Glasgow Coma Scale (MGCS) score in dogs with altered level of consciousness (ALOC).

DESIGN

This prospective, observational, clinical study was conducted from February 2016 to March 2017, and follow-up was conducted until the death of dogs or their discharge from the hospital.

SETTING

This study was performed at the Small Animal Teaching Hospital.

ANIMALS

A total of 31 client-owned dogs (males, 20; females, 11) with ALOC and MGCS score <18 with no restrictions for age, breed, sex, and body weight were included. Dogs that received neuromuscular blocking agents before MGCS score evaluation were excluded.

INTERVENTIONS

BIS values were measured using the Covidien BIS Loc 2 Channel OEM module and a pediatric 4 sensor with a bifrontal application pattern.

MEASUREMENTS AND MAIN RESULTS

Minimal databases of initial neurological assessment, blood profiles, and chest and skull radiographs were developed. In addition, MGCS scores and BIS values were recorded. The mean BIS values for mild, moderate, and severe brain injuries were 89.14 ± 6.52, 77.21 ± 9.82, and 50.58 ± 27.04, respectively. Correlation analysis revealed a significantly positive relationship between BIS values and MGCS scores (r = 0.75; P < 0.001).

CONCLUSIONS

The significant correlation observed between MGCS scores and BIS values in dogs with ALOC demonstrated the usefulness of BIS as an alternative to MGCS for monitoring consciousness in patients with ALOC caused by traumatic brain injury, encephalitis, etc.

Characterizing Awake and Anesthetized States Using a Dimensionality Reduction Method

Distinguishing between awake and anesthetized states is one of the important problems in surgery. Vital signals contain valuable information that can be used in prediction of different levels of anesthesia. Some monitors based on electroencephalogram (EEG) such as the Bispectral (BIS) index have been proposed in recent years. This study proposes a new method for characterizing between awake and anesthetized states. We validated our method by obtaining data from 25 patients during the cardiac surgery that requires cardiopulmonary bypass. At first, some linear and non-linear features are extracted from EEG signals. Then a method called "LLE"(Locally Linear Embedding) is used to map high-dimensional features in a three-dimensional output space. Finally, low dimensional data are used as an input to a quadratic discriminant analyzer (QDA). The experimental results indicate that an overall accuracy of 88.4 % can be obtained using this method for classifying the EEG signal into conscious and unconscious states for all patients. Considering the reliability of this method, we can develop a new EEG monitoring system that could assist the anesthesiologists to estimate the depth of anesthesia accurately.

Measuring and reflecting depth of anesthesia using wavelet and power spectral density

This paper evaluates depth of anesthesia (DoA) monitoring using a new index. The proposed method preconditions raw EEG data using an adaptive threshold technique to remove spikes and low-frequency noise. We also propose an adaptive window length technique to adjust the length of the sliding window. The information pertinent to DoA is then extracted to develop a feature function using discrete wavelet transform and power spectral density. The evaluation demonstrates that the new index reflects the patient's transition from consciousness to unconsciousness with the induction of anesthesia in real time.