Heart disease is the leading cause of death for both men and women, according to the Centers for Disease Control and Prevention (CDC). In the U.S., one in every four deaths is a result of heart disease, which includes a range of conditions from arrhythmias, or abnormal heart rhythms, to defects, as well as blood vessel diseases, more commonly known as cardiovascular diseases.
Predicting and monitoring cardiovascular disease is often expensive and tenuous, involving high-tech equipment and intrusive procedures. However, a 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.
Arterial stiffening, in which arteries become less elastic and more rigid, can result in increased blood and pulse pressure. In addition to being a known risk factor for cardiovascular diseases, it is also associated with diseases like diabetes and renal failure.
By 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, which is expensive and often not feasible, or tonometry, which requires two pressure measurements and an electrocardiogram to match the phases of the two pressure waves.
The method developed by Pahlevan, Marianne Razavi and Peyman Tavallali uses a single, uncalibrated carotid pressure wave that can be captured with a smart phone’s camera. In a previous study, the team used the same technology to develop an iPhone app that can detect heart failure using the slight perturbations of your pulse beneath your skin to record a pulse wave. In the same fashion, they are able to determine arterial stiffness.
Instead of a detailed waveform required with tonometry, their method needs just the shape of a patient’s pulse wave for the mathematical model, called intrinsic frequency, to calculate key variables related to the phases of the patient’s heartbeat. These variables are then used in a machine learning model that determines pulse wave velocity (PWV) and, therefore, arterial stiffness.
To validate their method, they 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 measurements and compared them with the tonometry measurements from the study, finding an 85% correlation between the two.
But more importantly, they needed to determine whether their method could be used to predict cardiovascular disease.
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. Their study was published in Scientific Reports in January.