title: Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks.
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.
authors.affiliation: Department of Mechanical Engineering and Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan, Chung-Li 32003, Taiwan.
authors.forename: Muammar
authors.initials: M
authors.lastname: Sadrawi
authors.affiliation: Department of Anestheology, College of Medicine, National Taiwan University, Taipei 100, Taiwan.
authors.forename: Shou-Zen
authors.initials: SZ
authors.lastname: Fan
authors.affiliation: Department of Electronic and Computer Engineering, Brunel University London, Uxbridge UB8 3PH, UK.
authors.forename: Maysam F
authors.initials: MF
authors.lastname: Abbod
authors.affiliation: Missile & Rocket System Research Division, National Chung-Shan Institute of Science and Technology, Taoyuan, Longtan 32500, Taiwan.
authors.forename: Kuo-Kuang
authors.initials: KK
authors.lastname: Jen
authors.affiliation: Department of Mechanical Engineering and Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan, Chung-Li 32003, Taiwan ; Center of Biomarkers and Translational Medicine, National Central University, Chung-Li 32001, Taiwan.
authors.forename: Jiann-Shing
authors.initials: JS
authors.lastname: Shieh
mesh_terms: D000328:Adult; D018681:Anesthetics, General / Q000008:administration & dosage*; D003243:Consciousness / Q000187:drug effects* / Q000502:physiology; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D004562:Electrocardiography / Q000379:methods; D004569:Electroencephalography / Q000379:methods; D005260:Female; D006801:Humans; D064795:Intraoperative Neurophysiological Monitoring / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009415:Nerve Net; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D055986:Vital Signs / Q000187:drug effects* / Q000502:physiology
chemical_list: D018681:Anesthetics, General