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 (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, involving high-tech equipment and intrusive procedures. A new method was developed that couples a machine learning model with a patient's pulse data to measure a key risk factor for cardiovascular diseases and arterial stiffness using just a smartphone.
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 new method uses a single, uncalibrated carotid pressure wave that can be captured with a smartphone's camera. In a previous study, the same technology was used to develop an iPhone app that can detect heart failure using the slight perturbations of the pulse beneath the 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, the 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.
The machine learning method is able to capture clinically significant outcomes because of an intrinsic frequency algorithm — 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).