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.
|Title of host publication||Human-Like Machine Intelligence|
|Editors||Stephen Muggleton, Nicholas Chater|
|Publisher||Oxford University Press|
|Publication status||Published - 20 Sep 2021|
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Making ECGs intuitively explainable to the public for early detection of life-threatening heart conditions (ECG-X)
Caroline Jay (Participant), Alaa Alahmadi (Participant), Markel Vigo (Participant), Alan Davies (Participant), Jennifer Royle (Participant), Leanna Goodwin (Participant), Katharine Cresswell (Participant), Zahra Arain (Participant) & Katherine Dempsey (Participant)
Impact: Technological, Health and wellbeing