Monitoring depth of anesthesia using combination of EEG measure and hemodynamic variables

Abstract

Monitoring depth of anesthesia (DOA) via vital signs is a major ongoing challenge for anesthetists. A number of electroencephalogram (EEG)-based monitors such as the Bispectral (BIS) index have been proposed. However, anesthesia is related to central and autonomic nervous system functions whereas the EEG signal originates only from the central nervous system. This paper proposes an automated DOA detection system which consists of three steps. Initially, we introduce multiscale modified permutation entropy index which is robust in the characterization of the burst suppression pattern and combine multiscale information. This index quantifies the amount of complexity in EEG data and is computationally efficient, conceptually simple and artifact resistant. Then, autonomic nervous system activity is quantified with heart rate and mean arterial pressure which are easily acquired using routine monitoring machine. Finally, the extracted features are used as input to a linear discriminate analyzer (LDA). The method is validated with data obtained from 25 patients during the cardiac surgery requiring cardiopulmonary bypass. The experimental results indicate that an overall accuracy of 89.4 % can be obtained using combination of EEG measure and hemodynamic variables, together with LDA to classify the vital sign into awake, light, surgical and deep anesthetised states. The results demonstrate that the proposed method can estimate DOA more effectively than the commercial BIS index with a stronger artifact-resistance.

Keywords: Depth of anesthesia; Electroencephalogram (EEG); Hemodynamic parameters; Permutation entropy.

Figures

Fig. 1

Extraction of ordinal patterns from the EEG signal. While the algorithm moves consecutively through the EEG signal, the sections including of three data points are classified as one of the six possible patterns, demonstrated in the second row. On the top row of the diagram, a histogram of the relative numbers of each pattern in the EEG signal is shown

Fig. 2

Schematic explanation of the coarse-graining procedure for scale 2 and 3

Fig. 3

An example of the EEG measure and hemodynamic parameters changes that occur during increasing anesthetic drug effect for six patients. The circle symbols are used to show the time of loss of consciousness

Fig. 4

Histogram of error distribution for all epochs between MMPE and BIS/100 index for all patients (n = 25)

Fig. 5

Box plots of a Heart Rate (beats per minute) and b Mean Arterial Pressure (mmHg) values at the awake (I), Light anesthetised (II), surgical anesthetised (III) and deep anesthetised (IV) states for all patients (n = 25). The upper and lower lines of the ‘box’ refer to the 75th and 25th percentiles of the sample; the line in the middle of the box is the sample median, and the distance between the top and the bottom of the box is the interquartile range. Plus signs are cases with values that are 1.5 times greater than the interquartile range. The notches in the box are 95 % confidence intervals around the median of a sample

Fig. 6

Scatter plot of Mean Arterial Pressure (mmHg) and Heart Rate (beats per minute) values of four states of anesthesia for all patients (n = 25)