Detection of EEG burst-suppression in neurocritical care patients using an unsupervised machine learning algorithm

Narula, Gagan; Haeberlin, Marcellina Isabelle; Balsiger, J.; Strässle, Christian; Imbach, Lukas L.; Keller, Emanuela

doi:10.1016/j.clinph.2021.07.018

Cited by 9 publications

(6 citation statements)

References 20 publications

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“…The design of algorithms to detect burst suppression reliably remains an active area of research. 19,20 The BIS monitor, ibis , and ezibis employ a proprietary algorithm called QUAZI (a proprietary algorithm for burst suppression) to estimate EEG burst suppression, whereas the classical approach is to detect intervals in which the EEG voltage does not exceed 5 μV. 21 Openibis implements a baseline-compensated version of the simple 5 μV threshold which, over the EEG dataset studied, nevertheless produced very comparable results to QUAZI ( r = 0.996, Bland-Altman -0.40 ± 4.0, P very highly significant).…”

Section: Resultsmentioning

confidence: 99%

Open Reimplementation of the BIS Algorithms for Depth of Anesthesia

Connor

2022

Anesthesia &Amp; Analgesia

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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.

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Section: Resultsmentioning

confidence: 99%

Open Reimplementation of the BIS Algorithms for Depth of Anesthesia

Connor

2022

Anesthesia &Amp; Analgesia

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“…The authors found that a mean absolute error in estimating bursts-per-minute by the algorithm was approximately one burst, which would not be an important error in clinical practice. The performance of their unsupervised algorithm was comparable with a more advanced neural network model [42].…”

Section: Automated Tracking Of Sedation Delirium and Sleep With Elect...mentioning

confidence: 94%

Monitoring of sedation in mechanically ventilated patients using remote technology

Hanidziar

Westover

2022

Current Opinion in Critical Care

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Purpose of reviewTwo years of coronavirus disease 2019 (COVID-19) pandemic highlighted that excessive sedation in the ICU leading to coma and other adverse outcomes remains pervasive. There is a need to improve monitoring and management of sedation in mechanically ventilated patients. Remote technologies that are based on automated analysis of electroencephalogram (EEG) could enhance standard care and alert clinicians real-time when severe EEG suppression or other abnormal brain states are detected.Recent findingsHigh rates of drug-induced coma as well as delirium were found in several large cohorts of mechanically ventilated patients with COVID-19 pneumonia. In patients with acute respiratory distress syndrome, high doses of sedatives comparable to general anesthesia have been commonly administered without defined EEG endpoints. Continuous limited-channel EEG can reveal pathologic brain states such as burst suppression, that cannot be diagnosed by neurological examination alone. Recent studies documented that machine learning-based analysis of continuous EEG signal is feasible and that this approach can identify burst suppression as well as delirium with high specificity.SummaryPreventing oversedation in the ICU remains a challenge. Continuous monitoring of EEG activity, automated EEG analysis, and generation of alerts to clinicians may reduce drug-induced coma and potentially improve patient outcomes.

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“…The main purpose is to use various mathematical methods to strip components of different frequencies in the signal for targeted treatment. For example, in electrocardiogram (ECG) data processing, a five-minute moving average is often used for low-pass and high-pass filtering ( 29 , 115 , 116 ), and when building an EEG signal model, Narula et al also used a band-pass filter to remove baseline drift and high-frequency interference ( 117 ).…”

Section: Consensus Textmentioning

confidence: 99%

Chinese experts’ consensus on the application of intensive care big data

Su,

Liu,

Long

et al. 2024

Front. Med.

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The development of intensive care medicine is inseparable from the diversified monitoring data. Intensive care medicine has been closely integrated with data since its birth. Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible. Considering the need of standardization of application of big data in intensive care, Intensive Care Medicine Branch of China Health Information and Health Care Big Data Society, Standard Committee has convened expert group, secretary group and the external audit expert group to formulate Chinese Experts’ Consensus on the Application of Intensive Care Big Data (2022). This consensus makes 29 recommendations on the following five parts: Concept of intensive care big data, Important scientific issues, Standards and principles of database, Methodology in solving big data problems, Clinical application and safety consideration of intensive care big data. The consensus group believes this consensus is the starting step of application big data in the field of intensive care. More explorations and big data based retrospective research should be carried out in order to enhance safety and reliability of big data based models of critical care field.

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Detection of EEG burst-suppression in neurocritical care patients using an unsupervised machine learning algorithm

Cited by 9 publications

References 20 publications

Open Reimplementation of the BIS Algorithms for Depth of Anesthesia

Open Reimplementation of the BIS Algorithms for Depth of Anesthesia

Monitoring of sedation in mechanically ventilated patients using remote technology

Chinese experts’ consensus on the application of intensive care big data

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