A Real-Time Depth of Anesthesia Monitoring System Based on Deep Neural Network With Large EDO Tolerant EEG Analog Front-End

Park, Yongjae; Han, Su-Hyun; Byun, Wooseok; Kim, Jihoon; Lee, Hyung Chul; Kim, Seong‐Jin

doi:10.1109/tbcas.2020.2998172

Cited by 32 publications

(13 citation statements)

References 32 publications

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Zhan

Duan

et al. 2021

BMC Anesthesiol

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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, we hypothesize that heart rate variability (HRV)-derived features based on a deep neural network can distinguish different anaesthesia states, providing a secondary tool for DoA assessment. Methods A novel method of distinguishing different anaesthesia states was developed based on four HRV-derived features in the time and frequency domain 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 utilized 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 features in the time and frequency domain and a deep neural network could accurately distinguish between different anaesthesia states; however, this study is a pilot feasibility study. The proposed method—with other evaluation methods, such as EEG—is expected to assist anaesthesiologists in the accurate evaluation of the DoA.

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Section: Introductionmentioning

confidence: 99%

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

Zhan

Duan

et al. 2021

BMC Anesthesiol

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“…Most importantly, as rightfully stressed in Hartmann et al (2013) the presented method is unique as it is by design analyses an incoming stream of data datapoint-by-datapoint, thus allowing for true real-time utility, in contrast to real-time-like applications of offline methods that only built on the relatively short dynamics of data compared to available computational power (i.e., the desired estimates can be computed with an offline algorithm well before the next unit of data is received). Real-time analysis of data is required in many physiological applications, such as monitoring mental workload ( Myrden and Chau, 2017 ; Shafiei et al, 2020 ), depth of anesthesia ( Ha et al, 2018 ; Park et al, 2020 ), automated tracking of sleep stages ( Michielli et al, 2019 ), or brain computer interface applications ( Banville and Falk, 2016 ). Specifically, bivariate (or multivariate) analysis of neural recordings is the central concept of functional connectivity (FC) studies ( Bastos and Schoffelen, 2016 ).…”

Section: Discussionmentioning

confidence: 99%

Real-Time Algorithm for Detrended Cross-Correlation Analysis of Long-Range Coupled Processes

et al. 2022

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Assessing power-law cross-correlations between a pair – or among a set – of processes is of great significance in diverse fields of analyses ranging from neuroscience to financial markets. In most cases such analyses are computationally expensive and thus carried out offline once the entire signal is obtained. However, many applications – such as mental state monitoring or financial forecasting – call for fast algorithms capable of estimating scale-free coupling in real time. Detrended cross-correlation analysis (DCCA), a generalization of the detrended fluctuation analysis (DFA) to the bivariate domain, has been introduced as a method designed to quantify power-law cross-correlations between a pair of non-stationary signals. Later, in analogy with the Pearson cross-correlation coefficient, DCCA was adapted to the detrended cross-correlation coefficient (DCCC), however as of now no online algorithms were provided for either of these analysis techniques. Here we introduce a new formula for obtaining the scaling functions in real time for DCCA. Moreover, the formula can be generalized via matrix notation to obtain the scaling relationship between not only a pair of signals, but also all possible pairs among a set of signals at the same time. This includes parallel estimation of the DFA scaling function of each individual process as well, thus allowing also for real-time acquisition of DCCC. The proposed algorithm matches its offline variants in precision, while being substantially more efficient in terms of execution time. We demonstrate that the method can be utilized for mental state monitoring on multi-channel electroencephalographic recordings obtained in eyes-closed and eyes-open resting conditions.

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“…EEG data has shown great potential in research and commercial applications. It can be used as a diagnostic and monitoring tool for clinical applications, such as quantifying anesthesia levels before and during surgery [ 12 ], and film and advertising evaluation, such as film and television effect research [ 13 ]. However, there are many inherent challenges in EEG analysis, specifically the removal of various artifacts.…”

Section: Literaturementioning

confidence: 99%

Intelligent Method for Real-Time Portable EEG Artifact Annotation in Semiconstrained Environment Based on Computer Vision

Qian

Wang²,

Wang

et al. 2022

Computational Intelligence and Neuroscience

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As a convenient device for observing neural activity in the natural environment, portable EEG technology (PEEGT) has an extensive prospect in expanding neuroscience research into natural applications. However, unlike in the laboratory environment, PEEGT is usually applied in a semiconstrained environment, including management and engineering, generating much more artifacts caused by the subjects’ activities. Due to the limitations of existing artifacts annotation, the problem limits PEEGT to take advantage of portability and low-test cost, which is a crucial obstacle for the potential application of PEEGT in the natural environment. This paper proposes an intelligent method to identify two leading antecedent causes of EEG artifacts, participant’s blinks and head movements, and annotate the time segments of artifacts in real time based on computer vision (CV). Furthermore, it changes the original postprocessing mode based on artifact signal recognition to the preprocessing mode based on artifact behavior recognition by the CV method. Through a comparative experiment with three artifacts mark operators and the CV method, we verify the effectiveness of the method, which lays a foundation for accurate artifact removal in real time in the next step. It enlightens us on how to adopt computer technology to conduct large-scale neurotesting in a natural semiconstrained environment outside the laboratory without expensive laboratory equipment or high manual costs.

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A Real-Time Depth of Anesthesia Monitoring System Based on Deep Neural Network With Large EDO Tolerant EEG Analog Front-End

Cited by 32 publications

References 32 publications

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

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

Real-Time Algorithm for Detrended Cross-Correlation Analysis of Long-Range Coupled Processes

Intelligent Method for Real-Time Portable EEG Artifact Annotation in Semiconstrained Environment Based on Computer Vision

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