Signal Analysis for Neurocritical Patients

NCT03362346 | Completed

• 1 other identifier: 106152-E
• Study Type: observational
• Target: 156
• Locations: 1 country
• Sites: 1

Brief Summary

The project uses big data analysis techniques such as wavelet transform and deep learning to analyze physiological signals from neurocritical patients and build a model to evaluate intracranial condition and to predict neurological outcome. By identification of correlations among these parameters and their trends, we may achieve early detection of anomalies and enhance the ability in judgement of current neurological condition and prediction of prognosis. By continuous input of the past and contemporary data in the ICU, the model will be modified repeatedly and its accuracy improves as the model grows. The model can be used to recognize abnormalities earlier and provide a warning system. Clinicians taking care of neurocritical patients can adjust their treatment policy and evaluate the outcome according to such system.

Conditions

Brain Injuries, Acute

Keywords

Wavelet transform; Deep learning

Outcome Measures

Primary Outcomes (1)

• Neurological status

  Glasgow coma scale/Mortality

  Discharge out of the intensive care unit, averaged 2 weeks

Study Arms (1)

Neurocritical patients

Patients with brain injury from trauma, ischemic stroke, hemorrhage stroke (intracerebral hemorrhage, subarachnoid hemorrhage), brain tumor with increased intracranial pressure, brain infection, hydrocephalus, among others.

Device: intracranial pressure monitoring

Interventions

intracranial pressure monitoring DEVICE

The patients may have either intracranial pressure (ICP) monitor insertion or external ventricular drainage that can be used as ICP monitor.

Also known as: physiological monitoring

Neurocritical patients

Eligibility Criteria

• Age: 20 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Neurocritical patients admitted to intensive care unit (ICU), including but not limited to traumatic brain injury, hemorrhagic stroke, ischemic stroke, brain infection, brain tumor and acute hydrocephalus.

You may qualify if:

• Age equal to or older than 20 years
• Neurocritical patients admitted to intensive care unit (ICU), including but not limited to traumatic brain injury, hemorrhagic stroke, ischemic stroke, brain infection, brain tumor and acute hydrocephalus.
• Patients who have undergone cranial surgery and had intracranial pressure monitor inserted or external ventricular drainage. The central monitor of ICU is able to collect the data continuously

You may not qualify if:

• Age younger than 20 years.
• Continuous monitoring of intracranial pressure is not feasible.

Sponsors & Collaborators

• Far Eastern Memorial Hospital: lead

Study Sites (1)

Far Eastern Memorial Hospital

New Taipei City, 200, Taiwan

Location

Related Publications (7)

• Diederik P. Kingma and Jimmy Lei Ba. Adam: A method for stochastic optimization. Conference paper at ICLR 2015.

  BACKGROUND

• Michael Unser and Akram Aldroubi. (1996 Apr) A review of wavelets in biomedical applications. Proceedings of the IEEE 84(4): 626-638.

  BACKGROUND

• LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015 May 28;521(7553):436-44. doi: 10.1038/nature14539.

  PMID: 26017442 BACKGROUND

• Christopher Torrence and Gilbert P. Compo. (1998 Jan) A practical guide to wavelet analysis. Bulletin of the American Meteorological Society 79(1):61-78.

  BACKGROUND

• Theis, Fabian & Meyer-Base, Anke. (2010). Biomedical Signal Analysis - Contemporary Methods and Applications. Biomedical Signal Analysis: Contemporary Methods and Applications.

  BACKGROUND

• Min S, Lee B, Yoon S. Deep learning in bioinformatics. Brief Bioinform. 2017 Sep 1;18(5):851-869. doi: 10.1093/bib/bbw068.

  PMID: 27473064 BACKGROUND

• Yi Mao, Wenlin Chen, Yixin Chen, Chenyang Lu, Marin Kollef, and Thomas Bailey. (2012) An integrated data mining approach to real-time clinical monitoring and deterioration warning. Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining Pages 1140-1148. doi>10.1145/2339530.2339709

  BACKGROUND

MeSH Terms

Conditions

Brain Injuries

Interventions

Monitoring, Physiologic

Condition Hierarchy (Ancestors)

Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Craniocerebral Trauma; Trauma, Nervous System; Wounds and Injuries

Intervention Hierarchy (Ancestors)

Diagnostic Techniques and Procedures; Diagnosis

Study Officials

• Yi-Hsin Tsai, M.D.

  Far Eastern Memorial Hospital

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Chief of Neurointensive Care Unit

Study Record Dates

• First Submitted: November 29, 2017
• First Posted: December 5, 2017
• Study Start: December 18, 2017
• Primary Completion: May 24, 2018
• Study Completion: May 31, 2018
• Last Updated: June 6, 2018

Record last verified: 2018-06

Data Sharing

• IPD Sharing: Will not share

Locations

TW (1)