Monitoring the level of hypnosis using a hierarchical SVM system

Shalbaf, Ahmad; Shalbaf, Reza; Saffar, Mohsen; Sleigh, Jamie

doi:10.1007/s10877-019-00311-1

Cited by 28 publications

(24 citation statements)

References 42 publications

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“…The machine learning approach has been developed recently for advantages in performance and extensibility and has become indispensable for solving complex problems in most sciences [80][81][82]. This method is used to examine postoperative outcomes [83][84][85][86] and predict hypotension [87,88] and the depth of anesthesia [89][90][91][92][93][94]. Machine learning has also been applied in the fields of intensive care unit medicine [95], emergency medicine [96], and neuroimaging [97].…”

Section: Predicting Csa-aki By Machine Learningmentioning

confidence: 99%

Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches

Hansrivijit

Vallabhajosyula

Mekraksakit

et al. 2020

JCM

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Cardiac surgery-associated AKI (CSA-AKI) is common after cardiac surgery and has an adverse impact on short- and long-term mortality. Early identification of patients at high risk of CSA-AKI by applying risk prediction models allows clinicians to closely monitor these patients and initiate effective preventive and therapeutic approaches to lessen the incidence of AKI. Several risk prediction models and risk assessment scores have been developed for CSA-AKI. However, the definition of AKI and the variables utilized in these risk scores differ, making general utility complex. Recently, the utility of artificial intelligence coupled with machine learning, has generated much interest and many studies in clinical medicine, including CSA-AKI. In this article, we discussed the evolution of models established by machine learning approaches to predict CSA-AKI.

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Section: Predicting Csa-aki By Machine Learningmentioning

confidence: 99%

Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches

Hansrivijit

Vallabhajosyula

Mekraksakit

et al. 2020

JCM

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“…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.…”

Section: Most Of the Researches Have Assessed The Doa Based On Eeg Fementioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Jin

Duan

et al. 2020

Preprint

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Background: Estimating the depth of anaesthesia (DoA) is critical in modern anaesthetic practice. Multiple DoA monitors based on electroencephalograms (EEGs) have been widely used for DoA monitoring; however, these monitors may be inaccurate under certain conditions. In this work, the hypothesis that heart rate variability (HRV)-derived features based on a deep neural network can distinguish different anaesthesia states was investigated.Methods: A novel method of distinguishing different anaesthesia states was developed based on four HRV-derived time and frequency domain features combined with a deep neural network. Four features were extracted from an electrocardiogram, including the HRV high-frequency power, low-frequency power, high-to-low-frequency power ratio, and sample entropy. Next, these features were used as inputs for the deep neural network, which used the expert assessment of consciousness level as the reference output. Finally, the deep neural network was compared with the logistic regression, support vector machine, and decision tree models. The datasets of 23 anaesthesia patients were used to assess the proposed method.Results: The accuracies of the four models, in distinguishing the anaesthesia states, were 86.2% (logistic regression), 87.5% (support vector machine), 87.2% (decision tree), and 90.1% (deep neural network). The accuracy of deep neural network was higher than those of the logistic regression (p < 0.05), support vector machine (p < 0.05), and decision tree (p < 0.05) approaches. Our method outperformed the logistic regression, support vector machine, and decision tree methods.Conclusions: The incorporation of four HRV-derived time and frequency domain features and a deep neural network could accurately distinguish between different anaesthesia states; however, this study is a pilot of a feasibility study, providing a method to supplement DoA monitoring based on EEG features to improve the accuracy of DoA estimation.

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“…Ferreira et al used multiple features of the blink reflex frequency domain to perform a multi-class logistic regression (LR) to predict DoA in eleven patients who were subjected to propofol anaesthesia [30]. In the work of Shalbaf et al, a support vector machine (SVM) with Shannon permutation entropy and frequency features was used to estimate the DoA in seventeen patients who were subject to sevoflurane anaesthesia [31]. Lee et al used four EEG parameters, including the burst suppression ratio, power of electromyogram, 95% spectral edge frequency, and relative beta ratio to construct a deep decision tree (DT) to evaluate DoA [32].…”

Section: Introductionmentioning

confidence: 99%

Heart Rate Variability-Derived Features Based on Deep Neural Networks for Monitoring Depth of Anaesthesia

Zhan

Duan

et al. 2020

Preprint

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Background: Estimating the depth of anaesthesia (DoA) is critical in clinical anaesthesiology. Electroencephalograms (EEGs) have been widely used for monitoring the DoA; however, they may be inaccurate under certain conditions. Methods: In this study, we propose a novel method to evaluate the DoA based on multiple heart rate variability (HRV)-derived features combined with a discrete wavelet transform and deep neural networks (DNNs). Four features were extracted from an electrocardiogram, including the HRV high-frequency power, low-frequency power, high-to-low-frequency power ratio, and sample entropy. Next, these features were used as inputs for the DNN, which used the expert assessment of consciousness level as the reference output. Finally, the DNN was compared with the logistic regression (LR), support vector machine (SVM), and decision tree (DT) models. The data of 23 anaesthesia patients were used to assess the proposed method. Results: The results demonstrated that the accuracies of the four models, in distinguishing the anaesthesia states, were 86.2% (LR),87.5% (SVM),87.2% (DT), and 90.1%(DNN). Our method outperformed the LR, SVM, and DT methods.Conclusions: The proposed method could accurately distinguish between different anaesthesia states, thus, providing an alternative or supplementary method to EEG monitoring for the assessment of DoA.

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Monitoring the level of hypnosis using a hierarchical SVM system

Cited by 28 publications

References 42 publications

Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches

Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches

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

Heart Rate Variability-Derived Features Based on Deep Neural Networks for Monitoring Depth of Anaesthesia

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