Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/11621
Title: Computational depth of anesthesia via multiple vital signs based on artificial neural networks
Authors: Sadrawi, M
Fan, SZ
Abbod, MF
Jen, KK
Shieh, JS
Keywords: Depth of anesthesia;Artificial neural networks;Mean absolute error;Bispectral index
Issue Date: 2015
Publisher: Hindawi Publishing Corporation
Citation: BioMed Research International, 2015: 536863, (2015)
Abstract: 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.
URI: http://www.hindawi.com/journals/bmri/2015/536863/
http://bura.brunel.ac.uk/handle/2438/11621
DOI: http://dx.doi.org/10.1155/2015/536863
ISSN: 2314-6133
2314-6141
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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