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Signal Analysis for Neurocritical Patients

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details. Identifier: NCT03362346
Recruitment Status : Completed
First Posted : December 5, 2017
Last Update Posted : June 6, 2018
Information provided by (Responsible Party):
Yi-Hsin Tsai, Far Eastern Memorial Hospital

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.

Condition or disease Intervention/treatment
Brain Injuries, Acute Device: intracranial pressure monitoring

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Study Type : Observational
Actual Enrollment : 156 participants
Observational Model: Cohort
Time Perspective: Retrospective
Official Title: Analysis of Physiological Signals From Neurocritical Patients in Intensive Care Units Using Wavelet Transform and Deep Learning
Actual Study Start Date : December 18, 2017
Actual Primary Completion Date : May 24, 2018
Actual Study Completion Date : May 31, 2018

Group/Cohort Intervention/treatment
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
The patients may have either intracranial pressure (ICP) monitor insertion or external ventricular drainage that can be used as ICP monitor.
Other Name: physiological monitoring

Primary Outcome Measures :
  1. Neurological status [ Time Frame: Discharge out of the intensive care unit, averaged 2 weeks ]
    Glasgow coma scale/Mortality

Information from the National Library of Medicine

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Ages Eligible for Study:   20 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
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.

Inclusion Criteria:

  • 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

Exclusion Criteria:

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

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its identifier (NCT number): NCT03362346

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Far Eastern Memorial Hospital
New Taipei City, Taiwan, 200
Sponsors and Collaborators
Far Eastern Memorial Hospital
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Principal Investigator: Yi-Hsin Tsai, M.D. Far Eastern Memorial Hospital
Diederik P. Kingma and Jimmy Lei Ba. Adam: A method for stochastic optimization. Conference paper at ICLR 2015.
Michael Unser and Akram Aldroubi. (1996 Apr) A review of wavelets in biomedical applications. Proceedings of the IEEE 84(4): 626-638.
Christopher Torrence and Gilbert P. Compo. (1998 Jan) A practical guide to wavelet analysis. Bulletin of the American Meteorological Society 79(1):61-78.
Theis, Fabian & Meyer-Base, Anke. (2010). Biomedical Signal Analysis - Contemporary Methods and Applications. Biomedical Signal Analysis: Contemporary Methods and Applications.
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

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Responsible Party: Yi-Hsin Tsai, Chief of Neurointensive Care Unit, Far Eastern Memorial Hospital Identifier: NCT03362346    
Other Study ID Numbers: 106152-E
First Posted: December 5, 2017    Key Record Dates
Last Update Posted: June 6, 2018
Last Verified: June 2018
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Yi-Hsin Tsai, Far Eastern Memorial Hospital:
Wavelet transform
Deep learning
Additional relevant MeSH terms:
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Brain Injuries
Brain Diseases
Central Nervous System Diseases
Nervous System Diseases
Craniocerebral Trauma
Trauma, Nervous System
Wounds and Injuries