Signal Analysis for Neurocritical Patients

Brain Injuries, Acute Clinical Trial

Official title:

Analysis of Physiological Signals From Neurocritical Patients in Intensive Care Units Using Wavelet Transform and Deep Learning

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT03362346
• Other study ID #: 106152-E
• Status: Completed
• Start date: December 18, 2017
• Est. completion date: May 31, 2018

Study information

• Verified date: June 2018
• Source: Far Eastern Memorial Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 156
• Est. completion date: May 31, 2018
• Est. primary completion date: May 24, 2018
• Accepts healthy volunteers: No
• Gender: All
• Age group: 20 Years and older

Eligibility

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.

Related Conditions & MeSH terms

• Brain Injuries
• Brain Injuries, Acute

Intervention

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.

Locations

Country	Name	City	State
Taiwan	Far Eastern Memorial Hospital	New Taipei City

Sponsors (1)

Lead Sponsor	Collaborator
Far Eastern Memorial Hospital

Country where clinical trial is conducted

Taiwan, 

References & Publications (7)

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

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

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

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

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

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

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Neurological status	Glasgow coma scale/Mortality	Discharge out of the intensive care unit, averaged 2 weeks