Human-Machine Perception of Complex Signal Data

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Electrocardiograms (ECGs), which capture the electrical activity of the human heart, are widely used in clinical practice, and notoriously difficult to interpret. Whilst there have been attempts to automate their interpretation for several decades, human reading of the data presented visually remains the ‘gold standard’. We demonstrate how a visualisation technique that significantly improves human interpretation of ECG data can be used as a basis for an automated interpretation algorithm that is more accurate than current signal processing techniques, and has the benefit of the human and machine sharing the same representation of the data. We discuss the potential of the approach, in terms of its accuracy and acceptability in clinical practice.
Original languageEnglish
Title of host publicationHuman-Like Machine Intelligence
EditorsStephen Muggleton, Nicholas Chater
PublisherOxford University Press
Chapter12
ISBN (Electronic)9780198862536
ISBN (Print)9780198862536
Publication statusPublished - 20 Sept 2021

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