Prof. Dong-Joo Kim's research paper, Artifact removal from neurophysiological signals: impact on intracranial and arterial pressure monitoring in traumatic brain injury was accepted in Journal of Neurosurgery.
Title: Artifact removal from neurophysiological signals: impact on intracranial and arterial pressure monitoring in traumatic brain injury
OBJECTIVE Monitoring intracranial and arterial blood pressure (ICP and ABP, respectively) provides crucial information regarding the neurological status of traumatic brain injury (TBI) patients. However, these signals are often heavily affected by artifacts, which may significantly reduce the reliability of the clinical determinations derived from the signals. The goal of this work is to eliminate signal artifacts from continuous ICP and ABP monitoring via deep learning techniques and to assess the changes in the prognostic capacities of clinical parameters after artifact elimination.
METHODS The first 24 h of monitoring ICP and ABP in a total of 309 patients with TBI were retrospectively analyzed. An artifact elimination model for ICP and ABP was constructed via a stacked convolutional autoencoder (SCAE) and convolutional neural network (CNN) with 10-fold cross-validation tests. The prevalence and prognostic capacity of ICP-ABP-related clinical events were compared before and after artifact elimination.
RESULTS The proposed SCAE-CNN model exhibited reliable accuracy in eliminating ABP and ICP artifacts (net prediction rates of 97% and 94%, respectively). The prevalence of ICP-ABP-related clinical events (i.e., systemic hypotension, intracranial hypertension, cerebral hypoperfusion and poor cerebrovascular reactivity) all decreased significantly after artifact removal.
CONCLUSIONS The SCAE-CNN model can be reliably used to eliminate artifacts, which significantly improves the reliability and efficacy of ICP-ABP-derived clinical parameters for prognostic determinations after TBI.