Data Driven Investigation of Bispectral Index Algorithm

Abstract

Bispectral index (BIS), a useful marker of anaesthetic depth, is calculated by a statistical multivariate model using nonlinear functions of electroencephalography-based subparameters. However, only a portion of the proprietary algorithm has been identified. We investigated the BIS algorithm using clinical big data and machine learning techniques. Retrospective data from 5,427 patients who underwent BIS monitoring during general anaesthesia were used, of which 80% and 20% were used as training datasets and test datasets, respectively. A histogram of data points was plotted to define five BIS ranges representing the depth of anaesthesia. Decision tree analysis was performed to determine the electroencephalography subparameters and their thresholds for classifying five BIS ranges. Random sample consensus regression analyses were performed using the subparameters to derive multiple linear regression models of BIS calculation in five BIS ranges. The performance of the decision tree and regression models was externally validated with positive predictive value and median absolute error, respectively. A four-level depth decision tree was built with four subparameters such as burst suppression ratio, power of electromyogram, 95% spectral edge frequency, and relative beta ratio. Positive predictive values were 100%, 80%, 80%, 85% and 89% in the order of increasing BIS in the five BIS ranges. The average of median absolute errors of regression models was 4.1 as BIS value. A data driven BIS calculation algorithm using multiple electroencephalography subparameters with different weights depending on BIS ranges has been proposed. The results may help the anaesthesiologists interpret the erroneous BIS values observed during clinical practice.

Heart rate variability-derived features based on deep neural network for distinguishing different anaesthesia states

“…Most research has assessed the DoA based on EEG features and machine learning algorithms; however, few studies have distinguished different anaesthesia states using HRV-derived features based on machine learning algorithms. Several studies were developed to predict the DoA using combinations of multiple EEG features and logistic regression [31], support vector machine [32], decision tree [33], and artificial neural network [34]. We adopted a multidimensional approach using logistic regression, support vector machine, decision tree, and deep neural network methods and four HRV-derived features to distinguish different anaesthesia states.…”

“…Recently, several machine learning algorithms, including logistic regression [31], support vector machine [32], decision tree [33], artificial neural network [34], and deep neural network [35], have been utilized to assess DoA based on different time-and frequency-domain features of an EEG signal. These results indicate that it is necessary to combine multiple time and frequency domain features to improve DoA assessment methods.…”

Poincaré Plot Area of Gamma-Band EEG as a Measure of Emergence From Inhalational General Anesthesia

“…RBR is defined as the logarithmic ratio of gamma-range spectral power ( P 30 – 47 Hz ) to the spectral power of the 11–20 Hz frequency band ( P 11 – 20 Hz ). Several studies have indicated that BIS values are highly dependent on RBR when the value of BIS is greater than 60 ( Morimoto et al, 2004 ; Lee et al, 2019 ). SFS is the logarithm of the ratio of the bispectral power in the 40–47 Hz waveband to the bispectral power in the 0.5–47 Hz band ( Rampil, 1998 ).…”

Heart Rate Variability-Derived Features Based on Deep Neural Network for Distinguishing Different Anaesthesia States

“…however, few studies have distinguished different anaesthesia states using HRV-derived features based on machine learning algorithms. Several studies have been developed to predict the DoA using combinations of multiple EEG features and logistic regression [31], support vector machine [32], decision tree [33], and arti cial neural network [34] respectively. We took a multidimensional approach using logistic regression, support vector machine, decision tree, and deep neural network methods and four HRV-derived features to distinguish different anaesthesia states.…”

“…Recently, several machine learning algorithms, including logistic regression [31], support vector machine [32], decision tree [33], arti cial neural network [34], and deep neural network [35], have been utilized to assess DoA based on different time and frequency domain features of EEG signal. These results indicate that it is necessary to combine multiple time and frequency domain features to improve DoA assessment methods.…”