A team of researchers has used data-mining and machine-learning techniques to find subtle changes in electrical activity in the heart that can be used to predict potentially fatal heart attacks.
The team developed ways to scan huge volumes of data to find slight abnormalities — computational biomarkers — that indicate defects in the heart muscle and nervous system. These included looking for subtle variability in the shape of apparently normal-looking heartbeats over time; specific sequences of changes in heart rate; and a comparison of a patient’s long-term ECG signal with those of other patients with similar histories.
They found that looking for these particular biomarkers in addition to using the traditional assessment tools helped to predict 50 percent more deaths. The best thing is that the data is already routinely collected, so implementing the system would not be costly.
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