Early Warning Systems

in

Biomedical Signal Processing

davidc@robots.ox.ac.uk

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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