Self-witnessing coherent imaging for artifact removal and noise filtering

Online Motion-Artifact Removal in fNIRS Signals: Combined Square-Root Cubature Kalman Filter and Weighted Moving Average Model Approach

2020 20th International Conference on Control, Automation and Systems (ICCAS) ◽

10.23919/iccas50221.2020.9268412 ◽

2020 ◽

Author(s):

Ruisen Huang ◽

Dalin Yang ◽

Kunqiang Qing ◽

Keum-Shik Hong

Keyword(s):

Kalman Filter ◽

Moving Average ◽

Motion Artifact ◽

Square Root ◽

Average Model ◽

Artifact Removal ◽

Cubature Kalman Filter ◽

Moving Average Model ◽

Weighted Moving Average ◽

Model Approach

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Artifact removal from neurophysiological signals: impact on intracranial and arterial pressure monitoring in traumatic brain injury

Journal of Neurosurgery ◽

10.3171/2019.2.jns182260 ◽

2020 ◽

Vol 132 (6) ◽

pp. 1952-1960 ◽

Cited By ~ 1

Author(s):

Seung-Bo Lee ◽

Hakseung Kim ◽

Young-Tak Kim ◽

Frederick A. Zeiler ◽

Peter Smielewski ◽

...

Keyword(s):

Traumatic Brain Injury ◽

Brain Injury ◽

Neurological Status ◽

Cerebrovascular Reactivity ◽

Cerebral Hypoperfusion ◽

Clinical Parameters ◽

Artifact Removal ◽

Clinical Events ◽

Arterial Blood ◽

Before And After

OBJECTIVEMonitoring intracranial and arterial blood pressure (ICP and ABP, respectively) provides crucial information regarding the neurological status of patients with traumatic brain injury (TBI). 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 was 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.METHODSThe first 24 hours of monitoring ICP and ABP in a total of 309 patients with TBI was 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- and ABP-related clinical events were compared before and after artifact elimination.RESULTSThe 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- and ABP-related clinical events (i.e., systemic hypotension, intracranial hypertension, cerebral hypoperfusion, and poor cerebrovascular reactivity) all decreased significantly after artifact removal.CONCLUSIONSThe SCAE-CNN model can be reliably used to eliminate artifacts, which significantly improves the reliability and efficacy of ICP- and ABP-derived clinical parameters for prognostic determinations after TBI.

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