Hierarchical Poincaré analysis for anaesthesia monitoring

Hayase, Kazuma; Hayashi, Kazuko; Sawa, Teiji

doi:10.1007/s10877-019-00447-0

Cited by 6 publications

(6 citation statements)

References 45 publications

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“…The use of the above equation explains why response entropy and state entropy do not follow a straight line as a function of BSR. This is different from BIS, which is defined by its suppression ratio solely in the range of suppression ratio 40 to 100 6–9 …”

mentioning

confidence: 88%

“…In this case, high BIS values are most likely caused by epileptiform activity. A recent article with modern BIS Vista demonstrates multiple BIS values in the range 60 to 80 at low suppression ratio 6 . BIS and state entropy values in the range 40 to 60 have also been reported in sevoflurane anesthesia, 9 also when burst suppression was verified from EEG waveform 12 …”

mentioning

confidence: 91%

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Paradox of bispectral index and entropy during burst suppression

Särkelä

2021

European Journal of Anaesthesiology

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mentioning

confidence: 88%

mentioning

confidence: 91%

Paradox of bispectral index and entropy during burst suppression

Särkelä

2021

European Journal of Anaesthesiology

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“…In studies based on EEG signals, researchers have developed monitoring systems using EEG-based criteria to evaluate the depth of anesthesia more accurately (3,(74)(75)(76)(77)(78)(79)(80)(81)(82)(83)(84)(85)(86)(87)(88). The bispectral index (BIS) is a common diagnostic index used to measure the depth of anesthesia based on EEG signals.…”

Section: Physiological-clinical Variables and The Combination Of Eeg ...mentioning

confidence: 99%

“…In other studies, new indices or improved versions of the previous indices were defined to determine the depth of anesthesia and enhance monitoring (78,84). For instance, the Poincaré index was introduced to target a specific frequency range of 20 to 30 Hz, and it was combined with the classical Poincaré 0.5 -47 Hz index using DL-improved anesthesia depth monitoring (84).…”

Section: Physiological-clinical Variables and The Combination Of Eeg ...mentioning

confidence: 99%

Machine Learning-Guided Anesthesiology: A Review of Recent Advances and Clinical Applications

Hashemi,

Yousefzadeh,

Abin

et al. 2024

J Cell Mol Anesth

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: Anesthesia is the process of inducing and experiencing various conditions, such as painlessness, immobility, and amnesia, to facilitate surgeries and other medical procedures. During the administration of anesthesia, anesthesiologists face critical decision-making moments, considering the significance of the procedure and potential complications resulting from anesthesia-related choices. In recent years, artificial intelligence (AI) has emerged as a supportive tool for anesthesia decisions, given its potential to assist with control and management tasks. This study aims to conduct a comprehensive review of articles on the intersection of AI and anesthesia. A review was conducted by searching PubMed for peer-reviewed articles published between 2020 and early 2022, using keywords related to anesthesia and AI. The articles were categorized into nine distinct groups: "Depth of anesthesia", "Control of anesthesia delivery", "Control of mechanical ventilation and weaning", "Event prediction", "Ultrasound guidance", "Pain management", "Operating room logistic", "Monitoring", and "Neuro-critical care". Four reviewers meticulously examined the selected articles to extract relevant information. The studies within each category were reviewed by considering items such as the purpose and type of anesthesia, AI algorithms, dataset, data accessibility, and evaluation criteria. To enhance clarity, each category was analyzed with a higher resolution than previous review articles, providing readers with key points, limitations, and potential areas for future research to facilitate a better understanding of each concept. The advancements in AI techniques hold promise in significantly enhancing anesthesia practices and improving the overall experience for anesthesiologists.

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“…Poincaré plots were used to analyse the nonlinear dynamics of ECG signals [28][29][30][31][32]. Each point in the plot is specified as n = 1, 2, 3, …N (RR n , RR n + 1 ), where N represents the number of recorded R-waves.…”

Section: Cardiac Sympathetic Indexmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Kołodziej,

Majkowski,

Tarnowski

et al. 2021

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sympathetic index (CSI). CSI is based on the quantitative analyses of Poincare plot features. Several studies indicate that it is possible to use CSI to detect and predict seizures [14][15][16][17]. For CSI calculation, the method proposed by Toichi et al. in [18] is most often used. In this article, the symbolic name "Toichi" is used to refer to the algorithm described in this work. To determine CSI, first R-waves must be detected in the ECG signal. For this purpose, the well-known Pan-Tompkins algorithm [19] or another method can be used. Next, the RR intervals should be analysed using the Poincare plot [20].The purpose of the article is to use a 1D-convolutional neural network [21][22][23] to calculate the CSI values based on the registered ECG signal. For a trained neural network, there is no need to perform R-wave detection [24] and Poincare plot analysis. It was assumed that deep learning, with many training examples from many patients, would allow the network to work effectively. A 1D-convolutional neural network was used to estimate the CSI. Then it was checked whether the CSI was reliable for the prediction of epileptic seizures. It was also examined how the loss of part of the recorded signal or artifacts affected the calculation of the CSI using both the standard Toichi algorithm and the proposed method. The presented research is an extension of the study described in the conference paper [25]. MaterialsVideo EEG (vEEG) with simultaneous ECG was recorded in patients with refractory temporal epilepsy for long-term monitoring of epileptic seizures during presurgical evaluation. The signals were acquired at the Medical University of Warsaw.

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Hierarchical Poincaré analysis for anaesthesia monitoring

Cited by 6 publications

References 45 publications

Paradox of bispectral index and entropy during burst suppression

Paradox of bispectral index and entropy during burst suppression

Machine Learning-Guided Anesthesiology: A Review of Recent Advances and Clinical Applications

Bulletin of the Polish Academy of Sciences: Technical Sciences

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