dr. david a. clifton, college lecturer institute of biomedical engineering university of oxford
TRANSCRIPT
Early Warning Systems
in
Biomedical Signal Processing
Dr. David A. Clifton, College LecturerInstitute of Biomedical EngineeringUniversity of Oxford
I have a neural network
processor.
The problem 23,000 preventable cardiac
arrests occur every year in UK hospitals
20,000 readmissions into ICU every year – mortality 50%
The majority of these occur because physiological deterioration goes undetected – why?
Primitive warning systems
Level 3:ICU 1 : 1
Level 2: Step-down 1 : 4
Level 1: Acute wards 1 : 4
Level 0: General wards 1 : 10
Level -1: Home 1 : ?
Patient monitors generate very high numbers of false alerts (e.g. 86% of alerts)
The NHS response
Conventional univariate analysis
Existing methods apply simple thresholds to each parameter
Intolerant to transient noise Possibly not the appropriate domain (time ,
frequency) Where do we set these thresholds in a principled,
reliable manner?
Nurses & junior doctors trained to ignore alarms Rolls-Royce has deactivated conventional
automated methods
Intelligent early warning systems
Intelligent early warning systems
Available biosignals
EEG / GCSHeart rateBreathing rateSpO2Blood pressureTemperature
On a “good” day... Obvious
tachycardia Obvious
tachypnea Obvious
desaturations Obvious
hypotension Obviously
unconscious
Abnormalities were detected by clinicians,patient escalated.
Note the difficulties: Incomplete data Noisy data Varying sample
rates
On a “not-so-good” day...
Gradual deterioration
Is this patient gettingworse?
Should we make a call to emergency teams?
Patient unescalated,died soon after.
Intelligent early warning systems
How can we detect abnormality in patient biomedical signals?
How can we do it in a reliable way?
What are the pitfalls that we have to avoid?
How can we evaluate it?
In Hilary term... Plenty more to look forward to:
machine learning in biomedical engineering
In Hilary term...
Hardware Devices& Comms
Physiology & Clinical Issues
Commercial Solutions & Regulatory Issues
Signal Processing & Machine Learning
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