In a new study, researchers developed a simple new heart-monitoring technique to help people self-monitor their conditions at home without the need for hospital visits.
They built a simple technique that allows people to monitor their own electrocardiogram (ECG) for a potentially life-threatening condition.
The research was conducted by a team at the University of Manchester.
Previously people needed to have an ECG in the hospital that was interpreted by a highly-trained expert.
In the study, scientists show that if people apply color in the right way, they can easily monitor hospital-level health data themselves.
The “QT-interval” is the time it takes for the heart to depolarise and recharge itself.
Many common medications, including some prescribed for depression and cancer, can cause this to lengthen.
The longer it gets, the more likely you are to suffer from a life-threatening arrhythmia that can cause sudden death.
An ECG shows complex signal data representing the heart’s electrical activity. It is vital for detecting cardiac pathologies, but extremely difficult to interpret, even for clinicians.
The team has been working on a novel visualization technique that makes it straightforward for members of the general public to understand ECG data.
The newly developed technique works on a “single lead” ECG, which is the heart reading available on a smartwatch. A spectrum of color is applied to the area under the ECG signal from blue to red.
The more warm colors you can see, the greater the risk of long QT syndrome. Long QT syndrome often doesn’t 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 ability to accurately self-monitor conditions with confidence at home has the potential to ease the number of people requiring trips to the hospital, which could be particularly useful during the COVID-19 pandemic.
The technique is currently being used as the basis for a new Artificial Intelligence approach that can detect QT-interval lengthening automatically.
Because the algorithm uses a data representation that humans find easy to visually understand, it is not just explainable from a technical perspective, but also intuitively understandable.
One author of the study is Dr. Caroline Jay from The University of Manchester.
The study is published in PLoS One.
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