The Experts below are selected from a list of 48645 Experts worldwide ranked by ideXlab platform

Beatrice De Gelder - One of the best experts on this subject based on the ideXlab platform.

  • autonomic reactivity to sensory stimulation is related to Consciousness Level after severe traumatic brain injury
    Clinical Neurophysiology, 2006
    Co-Authors: V J M Wijnen, Matagne Heutink, Geert J M Van Boxtel, H J Eilander, Beatrice De Gelder
    Abstract:

    Abstract Objective To examine changes in the activity of the autonomic nervous system (ANS) that are related to recovery to Consciousness in the post-acute phase after severe traumatic brain injury (sTBI). Methods Skin conductance and heart rate reactivity to sensory stimulation were recorded every 2 weeks for an average period of 3.5 months in 16 adolescent patients, during the assessment of their Level of Consciousness (LoC), and their cognitive and functional behaviour. Results Both heart rate variability (HRV) and skin conductance Level (SCL) in reaction to sensory stimulation changed with recovery to Consciousness. Indices of HRV and SCL that represent sympathetic activity of the autonomic nervous system (ANS) increased with recovery, whereas indices that represent parasympathetic activity decreased. In addition, we observed an increase in sympathovagal balance of the ANS with recovery. Conclusions Recovery to Consciousness determined by clinical observation in sTBI in the post-acute phase is related to changes in SCL and HRV during sensory stimulation. ANS reactivity to environmental stimulation can therefore give objective supplementary information about the clinical state of sTBI patients, and can contribute to decision-making in the treatment policy of unresponsive patients. Significance These findings demonstrate that autonomic reactivity can be informative concerning how a severely damaged nervous system reacts to environmental stimulation and how, in a recovering nervous system, this reactivity changes.

Jiannshing Shieh - One of the best experts on this subject based on the ideXlab platform.

  • spectrum analysis of eeg signals using cnn to model patient s Consciousness Level based on anesthesiologists experience
    IEEE Access, 2019
    Co-Authors: Quan Liu, Shouzen Fan, Maysam F Abbod, Jiannshing Shieh, Jifa Cai, Yuchen Kung, Longsong Lin
    Abstract:

    One of the most challenging predictive data analysis efforts is an accurate prediction of depth of anesthesia (DOA) indicators which has attracted growing attention since it provides patients a safe surgical environment in case of secondary damage caused by intraoperative awareness or brain injury. However, many researchers put heavily handcraft feature extraction or carefully tailored feature engineering to each patient to achieve very high sensitivity and low false prediction rate for a particular dataset. This limits the benefit of the proposed approaches if a different dataset is used. Recently, representations learned using the deep convolutional neural network (CNN) for object recognition are becoming a widely used model of the processing hierarchy in the human visual system. The correspondence between models and brain signals that holds the acquired activity at high temporal resolution has been explored less exhaustively. In this paper, deep learning CNN with a range of different architectures is designed for identifying related activities from raw electroencephalography (EEG). Specifically, an improved short-time Fourier transform is used to stand for the time-frequency information after extracting the spectral images of the original EEG as input to CNN. Then CNN models are designed and trained to predict the DOA Levels from EEG spectrum without handcrafted features, which presents an intuitive mapping process with high efficiency and reliability. As a result, the best trained CNN model achieved an accuracy of 93.50%, interpreted as CNN’s deep learning to approximate the DOA by senior anesthesiologists, which highlights the potential of deep CNN combined with advanced visualization techniques for EEG-based brain mapping.

  • hrv derived data similarity and distribution index based on ensemble neural network for measuring depth of anaesthesia
    PeerJ, 2017
    Co-Authors: Quan Liu, Renchun Chiu, Shouzen Fan, Maysam F Abbod, Jiannshing Shieh
    Abstract:

    Evaluation of depth of anaesthesia (DoA) is critical in clinical surgery. Indices derived from electroencephalogram (EEG) are currently widely used to quantify DoA. However, there are known to be inaccurate under certain conditions; therefore, experienced anaesthesiologists rely on the monitoring of vital signs such as body temperature, pulse rate, respiration rate, and blood pressure to control the procedure. Because of the lack of an ideal approach for quantifying Level of Consciousness, studies have been conducted to develop improved methods of measuring DoA. In this study, a short-term index known as the similarity and distribution index (SDI) is proposed. The SDI is generated using heart rate variability (HRV) in the time domain and is based on observations of data distribution differences between two consecutive 32 s HRV data segments. A comparison between SDI results and expert assessments of Consciousness Level revealed that the SDI has strong correlation with anaesthetic depth. To optimise the effect, artificial neural network (ANN) models were constructed to fit the SDI, and ANN blind cross-validation was conducted to overcome random errors and overfitting problems. An ensemble ANN was then employed and was discovered to provide favourable DoA assessment in comparison with commonly used Bispectral Index. This study demonstrated the effectiveness of this method of DoA assessment, and the results imply that it is feasible and meaningful to use the SDI to measure DoA with the additional use of other measurement methods, if appropriate.

  • improved spectrum analysis in eeg for measure of depth of anesthesia based on phase rectified signal averaging
    Physiological Measurement, 2017
    Co-Authors: Quan Liu, Shouzen Fan, Maysam F Abbod, Yifeng Chen, Jiannshing Shieh
    Abstract:

    The definition of the depth of anesthesia (DOA) is still controversial and its measurement is not completely standardized in modern anesthesia. Power spectral analysis is an important method for feature detection in electroencephalogram (EEG) signals. Several spectral parameters derived from EEG have been proposed for measuring DOA in clinical applications. In the present paper, an improved method based on phase-rectified signal averaging (PRSA) is designed to improve the predictive accuracy of relative alpha and beta power, a frequency band power ratio, total power, median frequency (MF), spectral edge frequency 95 (SEF95), and spectral entropy for assessing anesthetic drug effects. Fifty-six patients undergoing general anesthesia in an operating theatre are studied. All EEG signals are continuously recorded from the awake state to the end of the recovery state and then filtered using multivariate empirical mode decomposition (MEMD). All parameters are evaluated using the commercial bispectral index (BIS) and expert assessment of conscious Level (EACL), respectively. The ability to predict DOA is estimated using the area under the receiver-operator characteristics curve (AUC). All indicators based on the improved method can clearly discriminate the conscious state from the anesthetized state after filtration (p  <  0.05). A significantly larger mean AUC (p  <  0.05) shows that the improved method performs better than the conventional method to measure the DOA in most circumstances. Especially for raw EEG contaminated by artifacts, when the BIS index is used to indicate the Consciousness Level, the improvement is 7.37% (p  <  0.05), 9.04% (p  <  0.05), 18.46% (p  <  0.05), 27.73% (p  <  0.05), 14.65% (p  <  0.05), 2.52%, 5.38% and 6.24% (p  <  0.05) for relative alpha and beta power, power ratio, total power, MF, SEF, RE and SE, respectively. However, when the EACL is used to indicate the Consciousness Level, the improvement is 3.30% (p  <  0.05), 16.69% (p  <  0.05), 15.08% (p  <  0.05), 34.83% (p  <  0.05), 27.78% (p  <  0.05), 5.89% (p  <  0.05), 26.05% (p  <  0.05) and 23.42% (p  <  0.05). Spectral parameters derived from PRSA are more useful to measure the DOA in noisy cases.

Quan Liu - One of the best experts on this subject based on the ideXlab platform.

  • spectrum analysis of eeg signals using cnn to model patient s Consciousness Level based on anesthesiologists experience
    IEEE Access, 2019
    Co-Authors: Quan Liu, Shouzen Fan, Maysam F Abbod, Jiannshing Shieh, Jifa Cai, Yuchen Kung, Longsong Lin
    Abstract:

    One of the most challenging predictive data analysis efforts is an accurate prediction of depth of anesthesia (DOA) indicators which has attracted growing attention since it provides patients a safe surgical environment in case of secondary damage caused by intraoperative awareness or brain injury. However, many researchers put heavily handcraft feature extraction or carefully tailored feature engineering to each patient to achieve very high sensitivity and low false prediction rate for a particular dataset. This limits the benefit of the proposed approaches if a different dataset is used. Recently, representations learned using the deep convolutional neural network (CNN) for object recognition are becoming a widely used model of the processing hierarchy in the human visual system. The correspondence between models and brain signals that holds the acquired activity at high temporal resolution has been explored less exhaustively. In this paper, deep learning CNN with a range of different architectures is designed for identifying related activities from raw electroencephalography (EEG). Specifically, an improved short-time Fourier transform is used to stand for the time-frequency information after extracting the spectral images of the original EEG as input to CNN. Then CNN models are designed and trained to predict the DOA Levels from EEG spectrum without handcrafted features, which presents an intuitive mapping process with high efficiency and reliability. As a result, the best trained CNN model achieved an accuracy of 93.50%, interpreted as CNN’s deep learning to approximate the DOA by senior anesthesiologists, which highlights the potential of deep CNN combined with advanced visualization techniques for EEG-based brain mapping.

  • hrv derived data similarity and distribution index based on ensemble neural network for measuring depth of anaesthesia
    PeerJ, 2017
    Co-Authors: Quan Liu, Renchun Chiu, Shouzen Fan, Maysam F Abbod, Jiannshing Shieh
    Abstract:

    Evaluation of depth of anaesthesia (DoA) is critical in clinical surgery. Indices derived from electroencephalogram (EEG) are currently widely used to quantify DoA. However, there are known to be inaccurate under certain conditions; therefore, experienced anaesthesiologists rely on the monitoring of vital signs such as body temperature, pulse rate, respiration rate, and blood pressure to control the procedure. Because of the lack of an ideal approach for quantifying Level of Consciousness, studies have been conducted to develop improved methods of measuring DoA. In this study, a short-term index known as the similarity and distribution index (SDI) is proposed. The SDI is generated using heart rate variability (HRV) in the time domain and is based on observations of data distribution differences between two consecutive 32 s HRV data segments. A comparison between SDI results and expert assessments of Consciousness Level revealed that the SDI has strong correlation with anaesthetic depth. To optimise the effect, artificial neural network (ANN) models were constructed to fit the SDI, and ANN blind cross-validation was conducted to overcome random errors and overfitting problems. An ensemble ANN was then employed and was discovered to provide favourable DoA assessment in comparison with commonly used Bispectral Index. This study demonstrated the effectiveness of this method of DoA assessment, and the results imply that it is feasible and meaningful to use the SDI to measure DoA with the additional use of other measurement methods, if appropriate.

  • improved spectrum analysis in eeg for measure of depth of anesthesia based on phase rectified signal averaging
    Physiological Measurement, 2017
    Co-Authors: Quan Liu, Shouzen Fan, Maysam F Abbod, Yifeng Chen, Jiannshing Shieh
    Abstract:

    The definition of the depth of anesthesia (DOA) is still controversial and its measurement is not completely standardized in modern anesthesia. Power spectral analysis is an important method for feature detection in electroencephalogram (EEG) signals. Several spectral parameters derived from EEG have been proposed for measuring DOA in clinical applications. In the present paper, an improved method based on phase-rectified signal averaging (PRSA) is designed to improve the predictive accuracy of relative alpha and beta power, a frequency band power ratio, total power, median frequency (MF), spectral edge frequency 95 (SEF95), and spectral entropy for assessing anesthetic drug effects. Fifty-six patients undergoing general anesthesia in an operating theatre are studied. All EEG signals are continuously recorded from the awake state to the end of the recovery state and then filtered using multivariate empirical mode decomposition (MEMD). All parameters are evaluated using the commercial bispectral index (BIS) and expert assessment of conscious Level (EACL), respectively. The ability to predict DOA is estimated using the area under the receiver-operator characteristics curve (AUC). All indicators based on the improved method can clearly discriminate the conscious state from the anesthetized state after filtration (p  <  0.05). A significantly larger mean AUC (p  <  0.05) shows that the improved method performs better than the conventional method to measure the DOA in most circumstances. Especially for raw EEG contaminated by artifacts, when the BIS index is used to indicate the Consciousness Level, the improvement is 7.37% (p  <  0.05), 9.04% (p  <  0.05), 18.46% (p  <  0.05), 27.73% (p  <  0.05), 14.65% (p  <  0.05), 2.52%, 5.38% and 6.24% (p  <  0.05) for relative alpha and beta power, power ratio, total power, MF, SEF, RE and SE, respectively. However, when the EACL is used to indicate the Consciousness Level, the improvement is 3.30% (p  <  0.05), 16.69% (p  <  0.05), 15.08% (p  <  0.05), 34.83% (p  <  0.05), 27.78% (p  <  0.05), 5.89% (p  <  0.05), 26.05% (p  <  0.05) and 23.42% (p  <  0.05). Spectral parameters derived from PRSA are more useful to measure the DOA in noisy cases.

Maysam F Abbod - One of the best experts on this subject based on the ideXlab platform.

  • spectrum analysis of eeg signals using cnn to model patient s Consciousness Level based on anesthesiologists experience
    IEEE Access, 2019
    Co-Authors: Quan Liu, Shouzen Fan, Maysam F Abbod, Jiannshing Shieh, Jifa Cai, Yuchen Kung, Longsong Lin
    Abstract:

    One of the most challenging predictive data analysis efforts is an accurate prediction of depth of anesthesia (DOA) indicators which has attracted growing attention since it provides patients a safe surgical environment in case of secondary damage caused by intraoperative awareness or brain injury. However, many researchers put heavily handcraft feature extraction or carefully tailored feature engineering to each patient to achieve very high sensitivity and low false prediction rate for a particular dataset. This limits the benefit of the proposed approaches if a different dataset is used. Recently, representations learned using the deep convolutional neural network (CNN) for object recognition are becoming a widely used model of the processing hierarchy in the human visual system. The correspondence between models and brain signals that holds the acquired activity at high temporal resolution has been explored less exhaustively. In this paper, deep learning CNN with a range of different architectures is designed for identifying related activities from raw electroencephalography (EEG). Specifically, an improved short-time Fourier transform is used to stand for the time-frequency information after extracting the spectral images of the original EEG as input to CNN. Then CNN models are designed and trained to predict the DOA Levels from EEG spectrum without handcrafted features, which presents an intuitive mapping process with high efficiency and reliability. As a result, the best trained CNN model achieved an accuracy of 93.50%, interpreted as CNN’s deep learning to approximate the DOA by senior anesthesiologists, which highlights the potential of deep CNN combined with advanced visualization techniques for EEG-based brain mapping.

  • hrv derived data similarity and distribution index based on ensemble neural network for measuring depth of anaesthesia
    PeerJ, 2017
    Co-Authors: Quan Liu, Renchun Chiu, Shouzen Fan, Maysam F Abbod, Jiannshing Shieh
    Abstract:

    Evaluation of depth of anaesthesia (DoA) is critical in clinical surgery. Indices derived from electroencephalogram (EEG) are currently widely used to quantify DoA. However, there are known to be inaccurate under certain conditions; therefore, experienced anaesthesiologists rely on the monitoring of vital signs such as body temperature, pulse rate, respiration rate, and blood pressure to control the procedure. Because of the lack of an ideal approach for quantifying Level of Consciousness, studies have been conducted to develop improved methods of measuring DoA. In this study, a short-term index known as the similarity and distribution index (SDI) is proposed. The SDI is generated using heart rate variability (HRV) in the time domain and is based on observations of data distribution differences between two consecutive 32 s HRV data segments. A comparison between SDI results and expert assessments of Consciousness Level revealed that the SDI has strong correlation with anaesthetic depth. To optimise the effect, artificial neural network (ANN) models were constructed to fit the SDI, and ANN blind cross-validation was conducted to overcome random errors and overfitting problems. An ensemble ANN was then employed and was discovered to provide favourable DoA assessment in comparison with commonly used Bispectral Index. This study demonstrated the effectiveness of this method of DoA assessment, and the results imply that it is feasible and meaningful to use the SDI to measure DoA with the additional use of other measurement methods, if appropriate.

  • improved spectrum analysis in eeg for measure of depth of anesthesia based on phase rectified signal averaging
    Physiological Measurement, 2017
    Co-Authors: Quan Liu, Shouzen Fan, Maysam F Abbod, Yifeng Chen, Jiannshing Shieh
    Abstract:

    The definition of the depth of anesthesia (DOA) is still controversial and its measurement is not completely standardized in modern anesthesia. Power spectral analysis is an important method for feature detection in electroencephalogram (EEG) signals. Several spectral parameters derived from EEG have been proposed for measuring DOA in clinical applications. In the present paper, an improved method based on phase-rectified signal averaging (PRSA) is designed to improve the predictive accuracy of relative alpha and beta power, a frequency band power ratio, total power, median frequency (MF), spectral edge frequency 95 (SEF95), and spectral entropy for assessing anesthetic drug effects. Fifty-six patients undergoing general anesthesia in an operating theatre are studied. All EEG signals are continuously recorded from the awake state to the end of the recovery state and then filtered using multivariate empirical mode decomposition (MEMD). All parameters are evaluated using the commercial bispectral index (BIS) and expert assessment of conscious Level (EACL), respectively. The ability to predict DOA is estimated using the area under the receiver-operator characteristics curve (AUC). All indicators based on the improved method can clearly discriminate the conscious state from the anesthetized state after filtration (p  <  0.05). A significantly larger mean AUC (p  <  0.05) shows that the improved method performs better than the conventional method to measure the DOA in most circumstances. Especially for raw EEG contaminated by artifacts, when the BIS index is used to indicate the Consciousness Level, the improvement is 7.37% (p  <  0.05), 9.04% (p  <  0.05), 18.46% (p  <  0.05), 27.73% (p  <  0.05), 14.65% (p  <  0.05), 2.52%, 5.38% and 6.24% (p  <  0.05) for relative alpha and beta power, power ratio, total power, MF, SEF, RE and SE, respectively. However, when the EACL is used to indicate the Consciousness Level, the improvement is 3.30% (p  <  0.05), 16.69% (p  <  0.05), 15.08% (p  <  0.05), 34.83% (p  <  0.05), 27.78% (p  <  0.05), 5.89% (p  <  0.05), 26.05% (p  <  0.05) and 23.42% (p  <  0.05). Spectral parameters derived from PRSA are more useful to measure the DOA in noisy cases.

  • sample entropy analysis of eeg signals via artificial neural networks to model patients Consciousness Level based on anesthesiologists experience
    BioMed Research International, 2015
    Co-Authors: George J A Jiang, Shouzen Fan, Maysam F Abbod, Huihsun Huang, Jheng Yan Lan, Fengfang Tsai, Hung Chi Chang, Yea Wen Yang, Fu Lan Chuang, Yi Fang Chiu
    Abstract:

    Electroencephalogram (EEG) signals, as it can express the human brain's activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA). Bispectral (BIS) index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD) method and analyzed using sample entropy (SampEn) analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN) model through using expert assessment of Consciousness Level (EACL) which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the Consciousness Level and reflects the DOA successfully.

Shouzen Fan - One of the best experts on this subject based on the ideXlab platform.

  • spectrum analysis of eeg signals using cnn to model patient s Consciousness Level based on anesthesiologists experience
    IEEE Access, 2019
    Co-Authors: Quan Liu, Shouzen Fan, Maysam F Abbod, Jiannshing Shieh, Jifa Cai, Yuchen Kung, Longsong Lin
    Abstract:

    One of the most challenging predictive data analysis efforts is an accurate prediction of depth of anesthesia (DOA) indicators which has attracted growing attention since it provides patients a safe surgical environment in case of secondary damage caused by intraoperative awareness or brain injury. However, many researchers put heavily handcraft feature extraction or carefully tailored feature engineering to each patient to achieve very high sensitivity and low false prediction rate for a particular dataset. This limits the benefit of the proposed approaches if a different dataset is used. Recently, representations learned using the deep convolutional neural network (CNN) for object recognition are becoming a widely used model of the processing hierarchy in the human visual system. The correspondence between models and brain signals that holds the acquired activity at high temporal resolution has been explored less exhaustively. In this paper, deep learning CNN with a range of different architectures is designed for identifying related activities from raw electroencephalography (EEG). Specifically, an improved short-time Fourier transform is used to stand for the time-frequency information after extracting the spectral images of the original EEG as input to CNN. Then CNN models are designed and trained to predict the DOA Levels from EEG spectrum without handcrafted features, which presents an intuitive mapping process with high efficiency and reliability. As a result, the best trained CNN model achieved an accuracy of 93.50%, interpreted as CNN’s deep learning to approximate the DOA by senior anesthesiologists, which highlights the potential of deep CNN combined with advanced visualization techniques for EEG-based brain mapping.

  • hrv derived data similarity and distribution index based on ensemble neural network for measuring depth of anaesthesia
    PeerJ, 2017
    Co-Authors: Quan Liu, Renchun Chiu, Shouzen Fan, Maysam F Abbod, Jiannshing Shieh
    Abstract:

    Evaluation of depth of anaesthesia (DoA) is critical in clinical surgery. Indices derived from electroencephalogram (EEG) are currently widely used to quantify DoA. However, there are known to be inaccurate under certain conditions; therefore, experienced anaesthesiologists rely on the monitoring of vital signs such as body temperature, pulse rate, respiration rate, and blood pressure to control the procedure. Because of the lack of an ideal approach for quantifying Level of Consciousness, studies have been conducted to develop improved methods of measuring DoA. In this study, a short-term index known as the similarity and distribution index (SDI) is proposed. The SDI is generated using heart rate variability (HRV) in the time domain and is based on observations of data distribution differences between two consecutive 32 s HRV data segments. A comparison between SDI results and expert assessments of Consciousness Level revealed that the SDI has strong correlation with anaesthetic depth. To optimise the effect, artificial neural network (ANN) models were constructed to fit the SDI, and ANN blind cross-validation was conducted to overcome random errors and overfitting problems. An ensemble ANN was then employed and was discovered to provide favourable DoA assessment in comparison with commonly used Bispectral Index. This study demonstrated the effectiveness of this method of DoA assessment, and the results imply that it is feasible and meaningful to use the SDI to measure DoA with the additional use of other measurement methods, if appropriate.

  • improved spectrum analysis in eeg for measure of depth of anesthesia based on phase rectified signal averaging
    Physiological Measurement, 2017
    Co-Authors: Quan Liu, Shouzen Fan, Maysam F Abbod, Yifeng Chen, Jiannshing Shieh
    Abstract:

    The definition of the depth of anesthesia (DOA) is still controversial and its measurement is not completely standardized in modern anesthesia. Power spectral analysis is an important method for feature detection in electroencephalogram (EEG) signals. Several spectral parameters derived from EEG have been proposed for measuring DOA in clinical applications. In the present paper, an improved method based on phase-rectified signal averaging (PRSA) is designed to improve the predictive accuracy of relative alpha and beta power, a frequency band power ratio, total power, median frequency (MF), spectral edge frequency 95 (SEF95), and spectral entropy for assessing anesthetic drug effects. Fifty-six patients undergoing general anesthesia in an operating theatre are studied. All EEG signals are continuously recorded from the awake state to the end of the recovery state and then filtered using multivariate empirical mode decomposition (MEMD). All parameters are evaluated using the commercial bispectral index (BIS) and expert assessment of conscious Level (EACL), respectively. The ability to predict DOA is estimated using the area under the receiver-operator characteristics curve (AUC). All indicators based on the improved method can clearly discriminate the conscious state from the anesthetized state after filtration (p  <  0.05). A significantly larger mean AUC (p  <  0.05) shows that the improved method performs better than the conventional method to measure the DOA in most circumstances. Especially for raw EEG contaminated by artifacts, when the BIS index is used to indicate the Consciousness Level, the improvement is 7.37% (p  <  0.05), 9.04% (p  <  0.05), 18.46% (p  <  0.05), 27.73% (p  <  0.05), 14.65% (p  <  0.05), 2.52%, 5.38% and 6.24% (p  <  0.05) for relative alpha and beta power, power ratio, total power, MF, SEF, RE and SE, respectively. However, when the EACL is used to indicate the Consciousness Level, the improvement is 3.30% (p  <  0.05), 16.69% (p  <  0.05), 15.08% (p  <  0.05), 34.83% (p  <  0.05), 27.78% (p  <  0.05), 5.89% (p  <  0.05), 26.05% (p  <  0.05) and 23.42% (p  <  0.05). Spectral parameters derived from PRSA are more useful to measure the DOA in noisy cases.

  • sample entropy analysis of eeg signals via artificial neural networks to model patients Consciousness Level based on anesthesiologists experience
    BioMed Research International, 2015
    Co-Authors: George J A Jiang, Shouzen Fan, Maysam F Abbod, Huihsun Huang, Jheng Yan Lan, Fengfang Tsai, Hung Chi Chang, Yea Wen Yang, Fu Lan Chuang, Yi Fang Chiu
    Abstract:

    Electroencephalogram (EEG) signals, as it can express the human brain's activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA). Bispectral (BIS) index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD) method and analyzed using sample entropy (SampEn) analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN) model through using expert assessment of Consciousness Level (EACL) which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the Consciousness Level and reflects the DOA successfully.