HRV-derived data similarity and distribution index based on ensemble neural network for measuring depth of anaesthesia

Evaluation of depth of anesthesia (DoA) accurately is always critical in clinical surgery. Traditionally, index derived from electroencephalogram (EEG) plays the dominant role to measure DoA. For lack of ideal approach to quantify the consciousness level when drugs are used like ketamine, nitrous oxide and so on, much many efforts are devoted to optimize the DoA measurement methods. In this study, 110 cases of physiological data are analyzed to predict DoA. We propose a short term index generated by heart rate variability (HRV) of electrocardiogram (ECG) called similarity index (SI). It represents the data difference complexity by observing two consecutive 32s HRV data segments. Compared with expert assessment of consciousness level (EACL) of DoA, it shows strong correlation with anesthetic depth. In order to optimize measure thise effect, artificial neural network (ANN) models are constructed to fit model SI. We also conduct tThe ANN model is developed absed on blind cross validation to overcome the random error of neural network. The results show that Furthermore, the ensemble ANN (EANN) presents better capability accuracy of DoA assessment. Our This research shows thatis HRV related SI parameter can be another an effective method for DoA evaluation. We It is believed that it is possible and meaningful to incorporate the SI to measure the DoA with other methods together if suitablywhen conditions allow