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According to the Centers for Disease Control and Prevention (CDC), heart disease is the leading cause of death; it includes abnormal heart rhythms, heart defects, blood vessel diseases and cardiovascular diseases. Predicting and monitoring cardiovascular disease is often expensive and tenuous, involving high-tech equipment and intrusive procedures.
New method developed by researchers at USC Viterbi School of Engineering offers a better way by coupling a machine learning model with a patient’s pulse data, they are able to measure a key risk factor for cardiovascular diseases and arterial stiffness, using just a smart phone. In arterial stiffening arteries become less elastic and more rigid, it can result in increased blood and pulse pressure, it is also associated with diabetes and renal failure.
If the aorta is stiff, then when it transfers the pulse energy all the way to the peripheral vasculature – to small vessels – it can cause organ damage. Measuring pulse wave velocity, which is the speed that the arterial pulse propagates through the circulatory system, clinicians are able to determine arterial stiffness. Current measurement methods include MRI and tonometry, which requires two pressure measurements and an electrocardiogram to match the phases of the two pressure waves.
In a previous study, the team used technology to develop an iPhone app that can detect heart failure using the slight perturbations of pulse beneath the skin to record a pulse wave and arterial stiffness. Researchers used existing tonometry data collected from the Framingham Heart Study, a long-term epidemiological cohort analysis.
Using 5,012 patients, they calculated their own PWV measurement and compared them with the tonometry measurements from the study, finding an 85 percent correlation between the two. Through a prospective study using 4,798 patients, they showed that their PWV measurement was significantly associated with the onset of cardiovascular diseases over a ten-year follow up period.
The reason their machine learning method is able to capture clinically significant outcomes is due to their intrinsic frequency algorithm, which is the mathematical analysis used to calculate physically relevant variables relating to the patient’s heart and vascular function. The main variables represent the heart’s performance during the contraction phase (systole) and the vasculature’s performance during the relaxed phase (diastole).
haleplushearty.org