Fig. 2.
Workflow for (real-time) computation of the anesthesia multimodal index of consciousness (AMIC). After data acquisition (including preprocessing, e.g., artifact detection), parameters (according to table 2) are extracted for each modality. Independent of the sampling rate of specific parameters (e.g., 10 s for electroencephalographic parameters and constant in case of patient age), each parameter is resampled at 5 s and synchronized in time to obtain a multivariate time series. In a first step, classification is performed in two independent models (model A trained for separation of consciousness and unconsciousness and model B trained for separation of different hypnotic states from wakefulness to burst suppression) using an Adaptive Neuro Fuzzy Inference System. Both models were trained to provide best fit of input data to paradigm A and B, leading to data reduction for further processing. In a second step, resulting indices A and B are combined by a third Adaptive Neuro Fuzzy Inference System model resulting in AMIC. ApEn = approximate entropy; BMI = body mass index; BS = burst suppression; HR = heart rate; MAC IE = monitored anesthesia care equivalent of difference between in- and end-expiratory gas concentration; MAP = mean arterial blood pressure; PeEn = permutation entropy; WSMF = weighted spectral median frequency.