Estimation of the depth of anesthesia by using a multioutput least-square support vector regression

Jahanseir, Mercedeh; Setarehdan, Seyed Kamaledin; Momenzadeh, Sirous

doi:10.3906/elk-1802-189

Cited by 2 publications

(2 citation statements)

References 23 publications

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“…Peker et al estimated the DoA by combining ReliefF feature selection and random forest algorithm [15]. Jahanseir et al estimated the DoA with a multi-output leastsquare support vector regression method [16]; Saadeh et al assessed the DoA with a machine learning fine decision tree classifier for DoA classification of 4 states (deep, moderate, and light DoA versus awake state) [17]. In addition to the above classical machine learning models, recently, many researchers employed deep learning as classification models for monitoring the DoA.…”

Section: Introductionmentioning

confidence: 99%

Inference of Brain States Under Anesthesia With Meta Learning Based Deep Learning Models

Wang

Liu

Wan

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

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Monitoring the depth of unconsciousnessduring 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.

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Section: Introductionmentioning

confidence: 99%

Inference of Brain States Under Anesthesia With Meta Learning Based Deep Learning Models

Wang

Liu

Wan

et al. 2022

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

show abstract

“…Peker et al estimated the DoA by combining ReliefF feature selection and random forest algorithm [14]. Jahanseir et al estimated the DoA with a multi-output leastsquare support vector regression method [15]; Saadeh et al assessed the DoA with a machine learning fine decision tree classifier for DoA classification of 4 states (deep, moderate, and light DoA versus awake state) [16]. In addition to the above classical machine learning models, recently, many researchers employed deep learning as classification models for monitoring the DoA.…”

Section: Introductionmentioning

confidence: 99%

Inference of Brain States under Anesthesia with Meta Learning Based Deep Learning Models

Wang

Liu

Wan

et al. 2021

Preprint

View full text Add to dashboard Cite

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.

show abstract

Estimation of the depth of anesthesia by using a multioutput least-square support vector regression

Cited by 2 publications

References 23 publications

Inference of Brain States Under Anesthesia With Meta Learning Based Deep Learning Models

Inference of Brain States Under Anesthesia With Meta Learning Based Deep Learning Models

Inference of Brain States under Anesthesia with Meta Learning Based Deep Learning Models

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