Frontal EEG Temporal and Spectral Dynamics Similarity Analysis between Propofol and Desflurane Induced Anesthesia Using Hilbert-Huang Transform

Liu, Quan; Ma, Li; Fan, Shou-Zen; Abbod, Maysam F.; Ai, Qing; Chen, Kun; Shieh, Jiann-Shing

doi:10.1155/2018/4939480

Cited by 13 publications

(13 citation statements)

References 60 publications

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“…Many studies have explored brain networks in different anesthetic states in humans based on EEG signals or fMRI [6][7][8]10], and explored the efficiency of information transmission in the brain using a graph-theoretic approach [33]. Many studies conclude that the functional connectivity will decrease with increasing sedation [34], which is contrary to our results.…”

Section: A Alterations Of Brain Networkcontrasting

confidence: 99%

“…Loss of consciousness is accompanied by changes in functional brain networks [5], and the efficiency of cortical information transmission decreases with increasing depth of anesthesia [6], especially significant in frontal-parietal interactions [7]. Recently, many scholars have used mathematical methods, such as phase lag index (PLI) [8], phase-amplitude coupling (PAC) [9], and phase locked values (PLV) [10] to construct functional connectivity networks in different states.…”

Section: Introductionmentioning

confidence: 99%

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A Two-Stream Graph Convolutional Network Based on Brain Connectivity for Anesthetized States Analysis

Chen

Xie

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Investigating neural mechanisms of anesthesia process and developing efficient anesthetized state detection methods are especially on high demand for clinical consciousness monitoring. Traditional anesthesia monitoring methods are not involved with the topological changes between electrodes covering the prefrontal-parietal cortices, by investigating electrocorticography (ECoG). To fill this gap, a framework based on the two-stream graph convolutional network (GCN) was proposed, i.e., one stream for extracting topological structure features, and the other one for extracting node features. The twostream graph convolutional network includes GCN Model 1 and GCN Model 2. For GCN Model 1, brain connectivity networks were constructed by using phase lag index (PLI), representing different structure features. A common adjacency matrix was founded through the dual-graph method, the structure features were expressed on nodes. Therefore, the traditional spectral graph convolutional network can be directly applied on the graphs with changing topological structures. On the other hand, the average of the absolute signal amplitudes was calculated as node features, then a fully connected matrix was constructed as the adjacency matrix of these node features, as the input of GCN Model 2. This method learns features of both topological structure and nodes of the graph, and uses a dual-graph approach to enhance the focus on topological structure features. Based on the ECoG signals of monkeys, results show that this method which can distinguish awake state, moderate sedation and deep sedation achieved an accuracy of 92.75% in group-level experiments and mean accuracy of 93.50% in subject-level experiments. Our work verifies the excellence of the graph convolutional network in anesthesia monitoring, the high recognition accuracy also shows that the brain network may carry neurological markers associated with anesthesia.

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Section: A Alterations Of Brain Networkcontrasting

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Two-Stream Graph Convolutional Network Based on Brain Connectivity for Anesthetized States Analysis

Chen

Xie

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…The diagnosis of pain is highly important when undergoing surgery or after surgical operation. To reduce the pain as explained in [20][21][22][23][66][67][68][69], general anesthesia is a safe and fundamental component for performing surgeries. Pain is monitored by anesthesiologists and a proper dosage of anesthesia is recommended according to the patients' health, age and type of surgery.…”

Section: Surgical/postoperative Painmentioning

confidence: 99%

“…The pain relates to the brain dynamics, and thus provides potential to trace differences in the brain's activity under different anesthetics. In [66][67][68][69], work was carried out on how to access the depth of anesthesia (DoA) levels using different signals from the brain, and how to relate it with the bispectral index (BIS) value to make a more convenient and easy way for the surgical operation to continue, as the patient will experience pain if they become awake.…”

Section: Surgical/postoperative Painmentioning

confidence: 99%

Explainable AI (XAI) Applied in Machine Learning for Pain Modeling: A Review

Madanu

Abbod

Hsiao³

et al. 2022

Technologies

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Pain is a complex term that describes various sensations that create discomfort in various ways or types inside the human body. Generally, pain has consequences that range from mild to severe in different organs of the body and will depend on the way it is caused, which could be an injury, illness or medical procedures including testing, surgeries or therapies, etc. With recent advances in artificial-intelligence (AI) systems associated in biomedical and healthcare settings, the contiguity of physician, clinician and patient has shortened. AI, however, has more scope to interpret the pain associated in patients with various conditions by using any physiological or behavioral changes. Facial expressions are considered to give much information that relates with emotions and pain, so clinicians consider these changes with high importance for assessing pain. This has been achieved in recent times with different machine-learning and deep-learning models. To accentuate the future scope and importance of AI in medical field, this study reviews the explainable AI (XAI) as increased attention is given to an automatic assessment of pain. This review discusses how these approaches are applied for different pain types.

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“…As neuroscientists and also as clinical neurophysiologists we ask ourselves if this new algorithm could improve or even change our understanding about the classical quantitative parameters of the EEG signal and also if its results have a physiological sense similar to those obtained with the FFT method; in other words, do these methods agree? To the best of our knowledge currently there is not sufficient evidence to answer those questions because the studies performed until now does not have enough healthy subjects, they consider just a few leads for the analysis or they actually do not take in consideration any agreement analysis 39‐43 . A right characterization of the normal EEG activity with a proper number of leads will be required for its introduction to daily medical assistance.…”

Section: Introductionmentioning

confidence: 99%

Comparison between traditional fast Fourier transform and marginal spectra using the Hilbert–Huang transform method for the broadband spectral analysis of the electroencephalogram in healthy humans

Arrufat-Pié

Estévez‐Báez

Estévez‐Carreras

et al. 2021

Engineering Reports

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The fast Fourier transform (FFT) has been the main tool for electroencephalographic (EEG) Spectral Analysis (SPA). However, as the EEG dynamics show nonlinear and non‐stationary behavior, results using the FFT approach may result meaningless. A novel method has been developed for the analysis of nonlinear and non‐stationary signals known as the Hilbert–Huang transform method. In this study we analyze the differences for the broadband (SPA) of the EEG using the traditional FFT approach with those calculated with the Hilbert Marginal Spectra (HMS) after decomposition of the EEG with a multivariate empirical mode decomposition algorithm. EEG segments recorded from 19 leads of 47 healthy volunteers were studied. Statistically significant differences between methods were found for almost all leads by variance analyses. The agreement assessment shows that mean weighted frequencies have a good agreement for almost all bands, with the exception of beta‐2 and gamma bands where values for the HMS where higher than 3 Hz. Also the HMS method received lower than 5% energy values for alpha activity with an increment in the adjacent bands. The HMS may be considered a good alternative for the SPA of the EEG when nonlinearity or non‐stationarity may be present.

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Frontal EEG Temporal and Spectral Dynamics Similarity Analysis between Propofol and Desflurane Induced Anesthesia Using Hilbert-Huang Transform

Cited by 13 publications

References 60 publications

A Two-Stream Graph Convolutional Network Based on Brain Connectivity for Anesthetized States Analysis

A Two-Stream Graph Convolutional Network Based on Brain Connectivity for Anesthetized States Analysis

Explainable AI (XAI) Applied in Machine Learning for Pain Modeling: A Review

Comparison between traditional fast Fourier transform and marginal spectra using the Hilbert–Huang transform method for the broadband spectral analysis of the electroencephalogram in healthy humans

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