Towards an Affordable Means of Surgical Depth of Anesthesia Monitoring: An EMG-ECG-EEG Case Study

The anesthetic dosing procedure is a key element of safe surgical practice, where it is paramount to ensure sufficient dosing of the anesthetic agent to the patient in order to reach the desired depth of sedation for the necessary procedure. One means of monitoring the depth of anesthesia (DoA) involves the use of the bispectral index (BIS), which decodes electroencephalography (EEG) signals acquired from the frontal cortex in a continuous fashion. The shortcomings of this include the complexity of the decoding of EEG signals, insensitivity to certain anesthetic agents, and the costly nature of the technology, which limits its adoption in resource-constrained settings. In this paper, we investigate an alternative source of physiological measurement modalities that can track DoA sufficiently while being much more affordable. Thus, we investigate this notion with the use of the University of Queensland database, which comprises EEG-EMG-ECG physiological data from patients going through a variety of surgical procedures. As part of this, select patient datasets were utilized in addition to a variety of signal decomposition and machine learning models—which totaled around 200 simulations—in order to investigate the most optimal combination of algorithms to track DoA using different physiological measurement modalities. The results showed that under certain algorithmic combinations and modeling processes, the ECG measurement (a ubiquitous monitor in anesthetic practice) can rival and occasionally surpass the accuracy of the EEG for DoA monitoring. In addition to this, we also propose a 2-phase modeling process that involves an algorithmic selection stage followed by a model deployment stage. Subsequent work in this area is advised to involve the acquisition of more physiological data from a broader mix of patients in order to further validate the consistency of the findings made in this study.