Cyber-Physical Platform for Preeclampsia Detection

TL;DR

This paper presents a cyber-physical system using an intelligent bracelet and machine learning for early detection of preeclampsia in pregnant women, leveraging wearable technology and smartphone integration.

Contribution

It introduces a novel wearable device with microfluidic sensors and a connected software system for monitoring hypertension in pregnancy, combining hardware and machine learning.

Findings

Prototype developed and tested with promising accuracy.

System enables real-time monitoring and data analysis.

Potential to improve early detection and outcomes in pregnancy.

Abstract

Hypertension-related conditions are the most prevalent complications of pregnancy worldwide. They manifest in up to 8% of cases and if left untreated, can lead to serious detrimental effects. Early detection of their sudden onset can help physicians alleviate the condition and improve outcomes for both would-be mother and baby. Today's prevalence of smartphones and cost-effective wearable technology provide new opportunities for individualized medicine. Existing devices promote heart health, they monitor and encourage physical activity and measure sleep quality. This builds interest and encourages users to require more advanced features. We believe these aspects form suitable conditions to create and market specialized wearable devices. The present paper details a cyber-physical system built around an intelligent bracelet for monitoring hypertension-related conditions tailored to pregnant women. The bracelet uses a microfluidic layer that is compressed by the blood pressing against the arterial wall. Integrated sensors register the waveform and send it to the user's smartphone, where the systolic and diastolic values are determined. The system is currently developed under European Union research funding, and includes a software server where data is stored and further processing is carried out through machine learning.