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Early Warning Systems in Biomedical Signal Processing davidc@robots.ox.ac.uk

Dr. David A. Clifton, College Lecturer Institute of Biomedical Engineering University of Oxford. Early Warning Systems in Biomedical Signal Processing davidc@robots.ox.ac.uk. I have a neural network processor. The problem.

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Early Warning Systems in Biomedical Signal Processing davidc@robots.ox.ac.uk

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  1. Dr. David A. Clifton, College Lecturer Institute of Biomedical Engineering University of Oxford Early Warning Systemsin Biomedical Signal Processingdavidc@robots.ox.ac.uk

  2. I have a neural network processor.

  3. 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?

  4. 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)

  5. The NHS response

  6. 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

  7. Intelligent early warning systems

  8. Intelligent early warning systems

  9. Available biosignals EEG / GCS Heart rate Breathing rate SpO2 Blood pressure Temperature

  10. 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

  11. 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.

  12. 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?

  13. In Hilary term... • Plenty more to look forward to:machine learning in biomedical engineering

  14. Signal Processing & Machine Learning Physiology & Clinical Issues In Hilary term... Project roles Hardware Devices& Comms Commercial Solutions & Regulatory Issues

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