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 language | English |
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Title of host publication | Human-Like Machine Intelligence |
Editors | Stephen Muggleton, Nicholas Chater |
Publisher | Oxford University Press |
Chapter | 12 |
ISBN (Electronic) | 9780198862536 |
ISBN (Print) | 9780198862536 |
Publication status | Published - 20 Sept 2021 |
Fingerprint
Dive into the research topics of 'Human-Machine Perception of Complex Signal Data'. Together they form a unique fingerprint.Impacts
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Exploiting an understanding of human visual perception to facilitate human-machine electrocardiogram interpretation of drug-induced long QT syndrome
Alahmadi, A. (Participant), Jay, C. (Participant), Vigo, M. (Participant), Davies, A. (Participant), (Participant), Goodwin, L. (Participant), (Participant), (Participant) & (Participant)
Impact: Technological, Health and wellbeing
Prizes
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Biotechnology & Medicine - Innovators Under 35 by MIT Technology Review
Alahmadi, A. (Recipient), 2022
Prize: Prize (including medals and awards)