Prediction of Abnormal Myocardial Relaxation From Signal Processed Surface ECG

“The heart has a pumping phase when it ejects blood and a relaxation phase when it fills. Our hypothesis was that if you could look into subtle changes in electrical signals during the relaxation phase and use machine learning to extract these subtle anomalies, could we predict the presence of early muscle dysfunction that would be normally diagnosed only using an echocardiogram. The study included two cohorts of patients, one from New York and another one in West Virginia, and found that the technique had robust diagnostic value for predicting muscle relaxation anomalies.”

“These data are extremely encouraging as they suggest a potential role of signal processed ECG in early cardiac disease detection. It is quite remarkable that MyoVista demonstrated a high diagnostic precision in detecting a state of cardiac muscle dysfunction only previously detectable using cardiac ultrasound techniques. This can eventually help in appropriate cardiac testing and reduce overall healthcare costs.”

“The current screening paradigm for heart disease is missing people at early stages, when diseases are most treatable. HeartSciences has taken a ubiquitous screening tool, the 12-lead ECG, and has, through the application of signal processing and artificial intelligence, turned it into a powerful tool that can detect diastolic dysfunction, an early indicator of most cardiac diseases.”