NCT03362346

Signal Analysis for Neurocritical Patients

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

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 156 (actual)
• Sponsor: Far Eastern Memorial Hospital · Academic / Other
• Sex: All
• Age: 20 Years
• Healthy volunteers: Not accepted

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

Interventions

Type	Name	Description
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.

Timeline

• Start date: 2017-12-18
• Primary completion: 2018-05-24
• Completion: 2018-05-31
• First posted: 2017-12-05
• Last updated: 2018-06-06

Locations

1 site across 1 country: Taiwan