A multi-institutional team of scientists led by Johns Hopkins cardiologist and biomedical engineer Hiroshi Ashikaga, M.D., has developed a mathematical model to measure and digitally map the beat-sustaining electrical flow between heart cells.
The work, the scientists say, could form a blueprint for vastly more precise imaging tests that capture cell-to-cell communication and pinpoint the clusters of cells at the epicenter of complex, life-threatening arrhythmias. Such imaging approaches would enable precision-targeted, minimally invasive treatments that eliminate rhythm-disrupting hotspots in the hearts electrical system.
At the heart of the new model is the idea that heart muscle cells act as analog-to-digital converters, taking up information from their surroundings, converting or interpreting the information, and transmitting the message to neighboring cells. Capturing and quantifying information transmitted from cell to cell can help catch aberrant signals that cause the heart to beat abnormally.
In their new model, the researchers created computer representations of normal and abnormal heartbeats, ranging from simpler benign arrhythmias with well-defined epicenters to dangerous rhythms that arise in multiple hotspots. They converted the electrical signals transmitted by cells into bits the zeroes and ones that are the basic units of information in computing and digital communications.
Next, they measured how much information was generated, transmitted and received during normal and abnormal heart rhythms and plotted the information onto a 2-D map to create an image of the arrhythmia.
The different types of arrhythmias generated markedly distinct profiles. By contrast, regular EKG tracings of the same rhythm disturbances looked similar with a lot of overlapping features, an observation suggesting that quantifying and digitizing information flow inside the heart would far more reliably distinguish one form of arrhythmia from another.
The study, published in The Journal of the Royal Society Interface, is available here.
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