ECG-X: Making ECGs explainable with colour to support early detection of life-threatening heart conditions

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"In a busy hospital, a junior A&E doctor is learning to interpret ECGs. Where once she would have been counting tiny squares and trying to manually determine the beginning and end of waves using trigonometry - for example drawing tangents - on a paper printout, instead she is exploring the data on an iPad, changing the axes and orientation of the ECG, applying colour to the area under the curve and zooming in to understand details of the signal. The visualisation tool she uses to understand the data also supports her in another way: providing an automated estimation of the likelihood of the conditions she is investigating, explaining in natural language how it has come to its judgement. Because the automated interpretation uses 'cognitive fit' - where the human and the machine share the same representation of the data - it is intuitively understandable, and easy to check and explore. In this case, the fit is provided by the visual presentation of the ECG data. The techniques that support human interpretation, which combine pre-attentive processing to highlight anomalies in the signal with clinical knowledge to ensure reliability and accuracy, are also used as the basis for the machine interpretation.""A patient has started taking a new medication for cancer treatment. He hasn't noticed it causing any side effects, but his smart watch has alerted him that the electrical activity of his heart may have changed, and advises him to consult a clinician urgently. He shows the data to his doctor in an emergency consultation, and has his medication changed. His heart activity soon returns to normal."The scenarios above show the transformation our research aims to achieve - moving from difficult manual interpretation of ECGs by experts, to self-monitoring at home to detect conditions that may lead to sudden cardiac death.The Electrocardiogram (ECG) is a graphical representation of the heart's electrical activity that is widely used in clinical practice for detecting cardiac pathologies. ECG interpretation is known to be complex, challenging both humans and machines. This research will develop a suite of visualisation techniques for interrogating ECG data, combining principles of visual perception with clinical knowledge to create decision support tools where humans and machines share the same representation of the data. The research will have two strands:In the first, visualisation techniques will be co-created with clinicians to develop reliable tools that will be trusted in clinical practice, and underpinning theory that can be used as a foundation for manipulating ECG data to assist interpretation, and support automated interpretation algorithms.In the second, selected forms of data presentation for specific conditions will be trialled and further developed with members of the public, to determine whether the techniques could potentially be used by lay people for monitoring their own cardiac health. Our long-term vision is to engineer clinically reliable and explainable human-like AI that will empower patients or their caregivers to intuitively self-monitor their ECGs for potentially life-threatening cardiac conditions outside the clinical setting. This vision ultimately aims to promote primary and secondary prevention of sudden cardiac death - a catastrophic event accounting for 50% of cardiovascular mortality, causing an estimated 300,000 deaths in the US and 60,000 deaths in the UK annually. Electrical problems with the heart leading to sudden cardiac death are often detectable only on an ECG, and the early signs of ischaemic heart disease can be detected on an ECG before other major symptoms occur. Improving ECG interpretation is thus essential for the earlier detection of potentially lethal heart conditions. Faster diagnosis and the ability to self-monitor will particularly benefit women, who often experience a delay in treatment due to different symptom presentation to men.