An artificial neural network model using multiple vital signs produced a lower mean absolute error and higher correlation coefficient for depth of anesthesia compared to the bispectral index.
Does an artificial neural network model using multiple vital signs improve the evaluation of depth of anesthesia compared to the bispectral index?
An artificial neural network model incorporating multiple vital signs, with EMG being the most significant parameter, provides a more accurate assessment of depth of anesthesia than the standard bispectral index.
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.
Sadrawi et al. (Thu,) conducted a other in Depth of anesthesia (n=63). Artificial neural network (ANN) model vs. Bispectral index (BIS) was evaluated on Mean absolute error (MAE) and correlation coefficient. An artificial neural network model using multiple vital signs produced a lower mean absolute error and higher correlation coefficient for depth of anesthesia compared to the bispectral index.