Open Reimplementation of the BIS Algorithms for Depth of Anesthesia

Abstract

Background: BIS (a brand of processed electroencephalogram [EEG] depth-of-anesthesia monitor) scores have become interwoven into clinical anesthesia care and research. Yet, the algorithms used by such monitors remain proprietary. We do not actually know what we are measuring. If we knew, we could better understand the clinical prognostic significance of deviations in the score and make greater research advances in closed-loop control or avoiding postoperative cognitive dysfunction or juvenile neurological injury. In previous work, an A-2000 BIS monitor was forensically disassembled and its algorithms (the BIS Engine) retrieved as machine code. Development of an emulator allowed BIS scores to be calculated from arbitrary EEG data for the first time. We now address the fundamental questions of how these algorithms function and what they represent physiologically.

Methods: EEG data were obtained during induction, maintenance, and emergence from 12 patients receiving customary anesthetic management for orthopedic, general, vascular, and neurosurgical procedures. These data were used to trigger the closely monitored execution of the various parts of the BIS Engine, allowing it to be reimplemented in a high-level language as an algorithm entitled ibis. Ibis was then rewritten for concision and physiological clarity to produce a novel completely clear-box depth-of-anesthesia algorithm titled openibis .

Results: The output of the ibis algorithm is functionally indistinguishable from the native BIS A-2000, with r = 0.9970 (0.9970-0.9971) and Bland-Altman mean difference between methods of -0.25 ± 2.6 on a unitless 0 to 100 depth-of-anesthesia scale. This precision exceeds the performance of any earlier attempt to reimplement the function of the BIS algorithms. The openibis algorithm also matches the output of the native algorithm very closely ( r = 0.9395 [0.9390-0.9400], Bland-Altman 2.62 ± 12.0) in only 64 lines of readable code whose function can be unambiguously related to observable features in the EEG signal. The operation of the openibis algorithm is described in an intuitive, graphical form.

Conclusions: The openibis algorithm finally provides definitive answers about the BIS: the reliance of the most important signal components on the low-gamma waveband and how these components are weighted against each other. Reverse engineering allows these conclusions to be reached with a clarity and precision that cannot be obtained by other means. These results contradict previous review articles that were believed to be authoritative: the BIS score does not appear to depend on a bispectral index at all. These results put clinical anesthesia research using depth-of-anesthesia scores on a firm footing by elucidating their physiological basis and enabling comparison to other animal models for mechanistic research.

Figure 1:

Comparative performance in time of the ibis (red), ezibis (green) and openibis (blue) algorithms with respect to the reference BIS A-2000 native depth-of-anesthesia algorithm (black), over the twelve patients studied. The black line is plotted thicker as otherwise it would be obscured by the accuracy of the other algorithms.

Figure 2:

Heatmap of the overall agreement between the depth-of-anesthesia algorithms. The red boxes indicate the customary clinical depth-of-anesthesia regions of burst suppression (0 – 20), deep hypnosis (20 – 40), general anesthesia (40 – 60), mild/moderate sedation (60 – 80) and awake (80 – 100). The red tramlines minimally bound these regions. (A) Agreement of the BIS A-2000 native algorithm with the ibis algorithm. (B) Agreement of the BIS A-2000 native algorithm with the ezibis algorithm. (C) Agreement of the BIS A-2000 native algorithm with the openibis algorithm.

Figure 3:

A graphical roadmap of EEG processing in the openibis algorithm, using data from patient 09 in Figure 1. (A) EEG epochs are converted into a power spectral density by Fourier transform. Subsequent calculations depend upon the relative power distributions across the whole frequency range, and across sub-bands spanning 0.5 – 4 Hz (low), 11 – 20 Hz (mid), 30 – 47 Hz (high) and 40 – 47 Hz (very high). (B) A component related to general anesthesia is derived from the difference in power concentration between the very high band and the spectrum as a whole. (C) The components related to sedation and general anesthesia are converted into depth-of-anesthesia scores using curvilinear logistic response functions. (D) The burst suppression rate (BSR) is converted linearly into a depth-of-anesthesia score. (E) The lower value of the sedation and general anesthesia scores predominates. Some transitional averaging between sedation and general occurs if the general score is lower and the difference between the low and mid band power is equivocal (≤ 5 dBμV). This score, representing the combination of sedation and general, is then weighted against the BSR score to produce a provisional final score. (F) Extreme values in the provisional score (≤ 10 or ≥ 97) are compressed into the allowable range of 0 – 100 to produce the final score.

Figure 4:

Public exhibition and demonstration of a new power spectral density display on the BIS monitor at the annual meeting of the ASA. (Photographed by the author, October 11^th 2021, San Diego, CA.)