Blood Pressure
Blood pressure (BP) monitoring is crucial for cardiovascular health, with research focusing on developing accurate and continuous non-invasive methods. Current efforts utilize machine learning, particularly deep neural networks (including transformers, convolutional neural networks, and generative adversarial networks), to estimate BP from various biosignals like photoplethysmography (PPG), electrocardiography (ECG), and even facial video analysis. These advancements aim to improve early detection of hypertension and other cardiovascular conditions, leading to better patient outcomes and potentially transforming healthcare through remote monitoring and personalized interventions.
Papers
Leveraging Large Language Models for Analyzing Blood Pressure Variations Across Biological Sex from Scientific Literature
Yuting Guo, Seyedeh Somayyeh Mousavi, Reza Sameni, Abeed Sarker
Learning from Two Decades of Blood Pressure Data: Demography-Specific Patterns Across 75 Million Patient Encounters
Seyedeh Somayyeh Mousavi, Yuting Guo, Abeed Sarker, Reza Sameni
Cuff-less Arterial Blood Pressure Waveform Synthesis from Single-site PPG using Transformer & Frequency-domain Learning
Muhammad Wasim Nawaz, Muhammad Ahmad Tahir, Ahsan Mehmood, Muhammad Mahboob Ur Rahman, Kashif Riaz, Qammer H. Abbasi
Phase-shifted remote photoplethysmography for estimating heart rate and blood pressure from facial video
Gyutae Hwang, Sang Jun Lee