{"id":1168,"date":"2019-05-04T10:44:18","date_gmt":"2019-05-04T09:44:18","guid":{"rendered":"https:\/\/www.mub.eps.manchester.ac.uk\/in-abstract\/?p=1168"},"modified":"2019-09-16T14:32:12","modified_gmt":"2019-09-16T13:32:12","slug":"medication-induced-ecg-changes","status":"publish","type":"post","link":"https:\/\/www.mub.eps.manchester.ac.uk\/in-abstract\/medication-induced-ecg-changes\/","title":{"rendered":"Evaluating the impact of pseudo-colour and coordinate system on the detection of medication-induced ECG changes"},"content":{"rendered":"

Using colour to visualise ECG data supports rapid detection of serious heart problems<\/strong><\/p>\n

The electrocardiogram (ECG), shows complex signal data representing the heart\u2019s electrical activity. It is vital for detecting cardiac pathologies, but extremely difficult to interpret, even for clinicians. Researchers at the University of Manchester have been working on a novel visualisation technique that makes it straightforward for members of the general public to understand ECG data.<\/p>\n

The research examines an important medical issue – long QT syndrome – which can be caused by many commonly prescribed medications. Long QT syndrome refers to an increased interval between two peaks (”Q” and ”T”) in the waveform of a heartbeat, which shows that it is taking longer than it should for the heart to repolarise. The syndrome can be very dangerous, leading to blackouts, palpitations, and potentially cardiac arrest.<\/p>\n

A psychophysical experiment systematically testing different forms of data presentation has shown that re-visualising ECG signal data using pseudo-colour and a polar coordinate system can support lay people in identifying increases in the QT interval. This result is important because it shows the potential for patients to self-monitor for long QT syndrome induced by medication. This can arise without warning and with no symptoms, so if patients can check for it themselves, it could be life-saving. The research has broad implications for how we visualise time-series data for supporting pattern recognition, and is now being used as the foundation for a new automated ”human-in-the-loop\u2019 algorithm for monitoring the QT interval.<\/p>\n

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