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Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks
Joint Authors
Shieh, Jiann-Shing
Jen, Kuo-Kuang
Sadrawi, Muammar
Abbod, Maysam F.
Fan, Shou-Zen
Source
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-10-13
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
This study evaluated the depth of anesthesia (DoA) index using artificial neural networks (ANN) which is performed as the modeling technique.
Totally 63-patient data is addressed, for both modeling and testing of 17 and 46 patients, respectively.
The empirical mode decomposition (EMD) is utilized to purify between the electroencephalography (EEG) signal and the noise.
The filtered EEG signal is subsequently extracted to achieve a sample entropy index by every 5-second signal.
Then, it is combined with other mean values of vital signs, that is, electromyography (EMG), heart rate (HR), pulse, systolic blood pressure (SBP), diastolic blood pressure (DBP), and signal quality index (SQI) to evaluate the DoA index as the input.
The 5 doctor scores are averaged to obtain an output index.
The mean absolute error (MAE) is utilized as the performance evaluation.
10-fold cross-validation is performed in order to generalize the model.
The ANN model is compared with the bispectral index (BIS).
The results show that the ANN is able to produce lower MAE than BIS.
For the correlation coefficient, ANN also has higher value than BIS tested on the 46-patient testing data.
Sensitivity analysis and cross-validation method are applied in advance.
The results state that EMG has the most effecting parameter, significantly.
American Psychological Association (APA)
Sadrawi, Muammar& Fan, Shou-Zen& Abbod, Maysam F.& Jen, Kuo-Kuang& Shieh, Jiann-Shing. 2015. Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks. BioMed Research International،Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1055863
Modern Language Association (MLA)
Sadrawi, Muammar…[et al.]. Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks. BioMed Research International No. 2015 (2015), pp.1-13.
https://search.emarefa.net/detail/BIM-1055863
American Medical Association (AMA)
Sadrawi, Muammar& Fan, Shou-Zen& Abbod, Maysam F.& Jen, Kuo-Kuang& Shieh, Jiann-Shing. Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks. BioMed Research International. 2015. Vol. 2015, no. 2015, pp.1-13.
https://search.emarefa.net/detail/BIM-1055863
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1055863