Manchester University researchers have developed a simple technique that allows people to monitor their own electrocardiogram (ECG) for drug induced long QT syndrome, a potentially life-threatening condition.
Previously people needed to have an ECG in hospital that was interpreted by a highly trained clinician. In new research published in PLoS One, scientists show that if colour is applied the right way then people can easily monitor hospital-level health data themselves.
An ECG shows complex signal data representing the heart’s electrical activity and is vital for detecting cardiac pathologies.
In a statement, Manchester University’s Dr Caroline Jay said: “For decades we’ve assumed that only medical experts can interpret ECGs. We now have evidence that if you display an ECG in the right way, it can easily be interpreted by a patient.”
The breakthrough could be valuable in evaluating the risk of long QT syndrome, a heart rhythm problem that occurs when the heart muscle takes longer than normal to recharge between beats. Many common medications can cause this, potentially leading to sudden death due to arrhythmia.
“Here, we’ve shown that it is simple for lay people to understand when they might be at risk of long QT syndrome,” said Dr Jay. “Empowering people to understand and monitor their own ECG is a huge leap forward for public health, as it will reduce the number of times people have to go into hospital for routine check-ups, and ensure they get emergency medical attention as soon as they need it.”
The newly developed technique works on a ‘single lead’ ECG, which is the heart reading available on a smart watch. A spectrum of colour is applied to the area under the ECG signal from blue to red. According to Manchester University, more warm colours indicate the greater the risk of long QT syndrome.
Long QT syndrome often does not cause symptoms, so an ECG is the only way to pick it up. Self-monitoring is particularly useful when someone starts taking a new form of medication, as they will be able to contact their doctor as soon as they notice an issue.
The technique is currently being used as the basis for a new Artificial Intelligence approach that can detect QT-interval lengthening automatically.