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![The standard VEP waveform in response to flash stimuli [17]](profile/Farid-Zand/publication/261240368/figure/fig2/AS:296899916648451@1447797864398/The-standard-VEP-waveform-in-response-to-flash-stimuli-17.png)
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![Fig. 3. The standard VEP waveform in response to flash stimuli [17]](profile/Farid-Zand/publication/261240368/figure/fig2/AS:296899916648451@1447797864398/The-standard-VEP-waveform-in-response-to-flash-stimuli-17_Q320.jpg)
![Fig. 4. Quadratic B-spline wavelet function [12]](profile/Farid-Zand/publication/261240368/figure/fig3/AS:296899916648452@1447797864423/Quadratic-B-spline-wavelet-function-12_Q320.jpg)
Recently, several attempts have been made to find the depth of anesthesia (DOA) by analyzing the ongoing electroencephalogram (EEG) signals during surgical operations. Nevertheless, specialists still do not rely on these indexes because they cannot accurately track the transitions of anesthetic depth. This paper presents an effective EEG-based inde...
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Citations
... In this method, the EEG signal was decomposed into different levels to extract the desired features, and an eigenvector of wavelet coefficients was calculated to develop a new index. Wavelet-weighted median frequency and wavelet coefficient energy entropy methods were used to develop new indexes with high rates of agreement with BIS [16]. Several studies commonly use permutation and sample entropy to create a correlated index with BIS [17,18]. ...
This paper proposed a new depth of anaesthesia (DoA) index for the real-time assessment of DoA using electroencephalography (EEG). In the proposed new DoA index, a wavelet transform threshold was applied to denoise raw EEG signals, and five features were extracted to construct classification models. Then, the Gaussian process regression model was employed for real-time assessment of anaesthesia states. The proposed real-time DoA index was implemented using a sliding window technique and validated using clinical EEG data recorded with the most popular commercial DoA product Bispectral Index monitor (BIS). The results are evaluated using the correlation coefficients and Bland–Altman methods. The outcomes show that the highest and the average correlation coefficients are 0.840 and 0.814, respectively, in the testing dataset. Meanwhile, the scatter plot of Bland–Altman shows that the agreement between BIS and the proposed index is 94.91%. In contrast, the proposed index is free from the electromyography (EMG) effect and surpasses the BIS performance when the signal quality indicator (SQI) is lower than 15, as the proposed index can display high correlation and reliable assessment results compared with clinic observations.
... The EEG signal across subjects can have high intra-class variability. Applying some signal processing methods, such as wavelet transformation [26], [27], Fourier transformation [28], [29], multitaper analysis [30] and artifacts removal algorithms [31], can reduce the impact of noise. However, there is no principled way to address the intrasubject variability. ...
Monitoring the depth of unconsciousness during anesthesia is beneficial in both clinical settings and neuroscience investigations to understand brain mechanisms. Electroencephalogram (EEG) has been used as an objective means of characterizing brain altered arousal and/or cognition states induced by anesthetics in real-time. Different general anesthetics affect cerebral electrical activities in different ways. However, the performance of conventional machine learning models on EEG data is unsatisfactory due to the low Signal to Noise Ratio (SNR) in the EEG signals, especially in the office-based anesthesia EEG setting. Deep learning models have been used widely in the field of Brain Computer Interface (BCI) to perform classification and pattern recognition tasks due to their capability of good generalization and handling noises. Compared to other BCI applications, where deep learning has demonstrated encouraging results, the deep learning approach for classifying different brain consciousness states under anesthesia has been much less investigated. In this paper, we propose a new framework based on meta-learning using deep neural networks, named
Anes-MetaNet
, to classify brain states under anesthetics. The Anes-MetaNet is composed of Convolutional Neural Networks (CNN) to extract power spectrum features, and a time consequence model based on Long Short-Term Memory (LSTM) networks to capture the temporal dependencies, and a meta-learning framework to handle large cross-subject variability. We use a multi-stage training paradigm to improve the performance, which is justified by visualizing the high-level feature mapping. Experiments on the office-based anesthesia EEG dataset demonstrate the effectiveness of our proposed Anes-MetaNet by comparison of existing methods.
... The EEG signals among subjects can have high intra-class variability. Applying some signal processing methods, such as wavelet transformation [26], [27], Fourier transformation [28], [29], multitaper analysis [30] and artifacts removal algorithms [31], can reduce the impact of noise. However, there is no principled way to address the inter-subject variability. ...
Monitoring the depth of unconsciousness during anesthesia is useful in both clinical settings and neuroscience investigations to understand brain mechanisms. Electroencephalogram (EEG) has been used as an objective means of characterizing brain altered arousal and/or cognition states induced by anesthetics in real-time. Different general anesthetics affect cerebral electrical activities in different ways. However, the performance of conventional machine learning models on EEG data is unsatisfactory due to the low Signal to Noise Ratio (SNR) in the EEG signals, especially in the office-based anesthesia EEG setting. Deep learning models have been used widely in the field of Brain Computer Interface (BCI) to perform classification and pattern recognition tasks due to their capability of good generalization and handling noises. Compared to other BCI applications, where deep learning has demonstrated encouraging results, the deep learning approach for classifying different brain consciousness states under anesthesia has been much less investigated. In this paper, we propose a new framework based on meta-learning using deep neural networks, named Anes-MetaNet, to classify brain states under anesthetics. The Anes-MetaNet is composed of Convolutional Neural Networks (CNN) to extract power spectrum features, and a time consequence model based on Long Short-Term Memory (LSTM) Networks to capture the temporal dependencies, and a meta-learning framework to handle large cross-subject variability. We used a multi-stage training paradigm to improve the performance, which is justified by visualizing the high-level feature mapping. Experiments on the office-based anesthesia EEG dataset demonstrate the effectiveness of our proposed Anes-MetaNet by comparison of existing methods.
... A wide range of features in diverse domains have been proposed for DOA assessment over past years. For example, wavelet coefficient energy entropy and wavelet weighted median frequency are introduced in order to obtain high correlated indexes with BIS [8], [9]. Burst suppression is a key feature to detect deep level of anesthesia. ...
Electroencephalography (EEG) is commonly used to measure the depth of anesthesia (DOA) because EEG reflects surgical pain and state of the brain. However, precise and real-time estimation of DOA index for painful surgical operations is challenging due to problems such as postoperative complications and accidental awareness. To tackle these problems, we propose a new combinatorial deep learning structure involving convolutional neural networks (inspired by the inception module), bidirectional long short-term memory, and an attention layer. The proposed model uses the EEG signal to continuously predicts the bispectral index (BIS). It is trained over a large dataset, mostly from those under general anesthesia with few cases receiving sedation/analgesia and spinal anesthesia. Despite the imbalance distribution of BIS values in different levels of anesthesia, our proposed structure achieves convincing root mean square error of 5.59 1.04 and mean absolute error of 4.3 0.87, as well as improvement in area under the curve of 15% on average, which surpasses state-of-the-art DOA estimation methods. The DOA values are also discretized into four levels of anesthesia and the results demonstrate strong inter-subject classification accuracy of 88.7% that outperforms the conventional methods.
... Pain-related evoked potentials (Eps) are a well-known modality in pain detection [15]. The reason is that the pain stimuli evoke, increases neural activity between 150 to 400 milliseconds after stimulus [16]. High overlap between many components of the Eps and the alpha band of EEG (8)(9)(10)(11)(12) Hz is the most challenging problem in this area. ...
Artificial intelligence (AI) is increasingly being used in clinical anesthesia, and researchers are using algorithms to dig information from patients’ perioperative data, process and analyze them from multi-dimensions, after which predictive models are built to dynamically predict perioperative adverse events.
This study aims to use combined wavelet and neural network model to extract electroencephalogram (EEG) signal features during anesthesia and classify them according to the Depth of Anaesthesia (DOA). EEG signals were selected according to their Bispectral Index (BIS) value during anaesthesia and were processed using Discrete Wavelet Transform (DWT).The dimensionality of the wavelet coefficient vectors were reduced by extracting key features from their distribution. Artificial Neural networks (ANN) were implemented using the extracted features of EEG signals as inputs and then classifying anesthetic depth as awake, light anesthesia, moderate anaesthesia, deep anesthesia and very deep anaesthesia. The proposed model will classify depth of anaesthesia accurately.
