The next generation of health

The next generation of health

High blood pressure (BP) remains the leading risk factor for death and disability in both high and low income countries1. Its complications are responsible for the deaths of approximately ten million people annually, a 50% increase over the estimates from 19902. By 2025, the number of people suffering from hypertension will reach 1.5 billion3. The impact of this disease represents a daunting burden to any healthcare system.

Mobile health diagnostics have been shown to be effective and scalable for chronic disease detection and management. By maximizing the smartphones’ optics and computational power, they could allow the assessment of physiological information from the morphology of pulse waves and thus estimate cuffless blood pressure (BP).

Application #1

We trained the parameters of an existing pulse wave analysis algorithm (oBPM), previously validated in anesthesia on pulse oximeter signals, by collecting optical signals from 51 patients fingertips via a smartphone while simultaneously acquiring BP measurements through an arterial catheter.

We then compared smartphone-based measurements obtained on 50 participants in an ambulatory setting via the OptiBP app against simultaneously acquired auscultatory systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean blood pressure (MBP) measurements. Patients were normotensive (70.0% for SBP versus 61.4% for DBP), hypertensive (17.1% vs. 13.6%) or hypotensive (12.9% vs. 25.0%).

The difference in BP (mean ± standard deviation) between both methods were within the ISO 81,060–2:2018 standard for SBP (− 0.7 ± 7.7 mmHg), DBP (− 0.4 ± 4.5 mmHg) and MBP (− 0.6 ± 5.2 mmHg). These results demonstrate that BP can be measured with accuracy at the finger using the OptiBP smartphone app.

This may become an important tool to detect hypertension in various settings, for example in low-income countries, where the availability of smartphones is high but access to health care is low.

Application #2

Biobeat’s solution uses health-AI and ML on big-data in order to provide actionable insights on patient care. More than just analyzing the data, Biobeat also generates it, using our proprietary sensor for continuous monitoring of vital signs unique to Biobeat. 

Biobeat is an Israeli developer of advanced wearable AI-powered remote patient monitoring (RPM) solutions for hospitals, long-term care, and pharma/CRO drug development & clinical trials. Used by healthcare facilities across the world, Biobeat’s patient monitors (wearable wrist and chest monitors) continuously collect patient health data (128 data points per second and more than 150 million individual data points per day) to empower health teams by providing a real-time accurate view of patient status. Biobeat utilizes AI, Big Data, and patient analysis to support health teams in providing optimal care to patients while minimizing the risk of infectious viral exposure from in-person spot-checks.

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