AI/ML Algorithm Training and Validation for the imPulse™ Tor System Self-Directed Systolic & Diastolic Blood Pressure Monitor.

Hypertension (HTN) is a major risk factor for cerebrovascular morbidity and mortality, yet its identification can be delayed due to lack of overt symptoms, relying on Blood Pressure (BP) measurements for diagnosis. Several BP monitoring techniques are used in clinics and hospitals, and there is also an outpatient method that is used for 24-hour BP monitoring, based on a sphygmomanometer. The general notion is that with frequent, reliable, self-directed BP monitoring in the clinic and at home, health care providers will be able to diagnose hypertension among individuals at an early stage, including high-risk patients in the community, and more quickly assess if prescribed treatment plans are working. However, non-invasive self-administered blood pressure monitoring continues to be a major challenge for automated vital sign measurement systems.

To circumvent the limitations of current systems of interval hemodynamic measurement and increasing demands on healthcare providers, a non-invasive automated self- directed vital sign monitor that can integrate into hospital early warning systems and warn healthcare providers of deteriorating vital sign parameters would be of significant value. This study aims to provide proof of concept for a portable device that measures vital signs (blood pressure, heart rate, and blood oxygen saturation) using biophysical pneumatics and hydraulics, vibroacoustics, and multi-lead ECG that aims to produce results that are as good as a sphygmomanometer-based device and pulse oximeter, without the need for a circumferential pressure device/sphygmomanometer on the arm.