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Machine learning-based blood pressure estimation using impedance cardiography data.

Acta Physiol (Oxf)

February 2025

Institute of Physiology, Center for Space Medicine and Extreme Environments Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany.

Objective: Accurate blood pressure (BP) measurement is crucial for the diagnosis, risk assessment, treatment decision-making, and monitoring of cardiovascular diseases. Unfortunately, cuff-based BP measurements suffer from inaccuracies and discomfort. This study is the first to access the feasibility of machine learning-based BP estimation using impedance cardiography (ICG) data.

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Unlabelled: Numerous studies highlight the significant role of exercise therapy in patients with peripheral artery disease (PAD), emphasizing how regular physical exercise enhances vascular endothelial function and promotes metabolic adaptations in skeletal muscles, ultimately improving walking performance. There are currently discussions in the medical world on optimizing noninvasive therapy to prevent the development of lower limb ischemia. This study aimed to assess the impact of a supervised training program combined with whirlpool massage treatment on improving peripheral circulation and physical performance in patients suffering from peripheral artery disease.

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Objectives: Early detection of cardiovascular diseases and their pre-existing conditions, arteriosclerosis and atherosclerosis, is crucial to increasing a patient's chance of survival. While imaging technologies and invasive procedures provide a reliable diagnosis, they carry high costs and risks for patients. This study aims to explore impedance plethysmography (IPG) as a non-invasive and affordable alternative for diagnosis.

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The long-term hypercortisolemia of Cushing disease (CD) may lead to hemodynamic disorders by increasing subclinical cardiac and vascular dysfunction. The purpose of this observational cohort study was to assess the relationship between hemodynamic parameters evaluated via impedance cardiography (ICG) and echocardiographic parameters reflecting left ventricular function in 54 patients newly diagnosed with CD. The parameters assessed via ICG included stroke volume index (SI), cardiac index (CI), acceleration index (ACI), velocity index (VI), systemic vascular resistance index (SVRI), total artery compliance index (TACI).

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Background: The body composition of National Collegiate Athletic Association (NCAA) athletes is well documented but no such data exist for university club sports athletes. Additionally, the majority of norms for NCAA athletes were created from individual methods requiring assumptions.

Objective: This study used a four-component (4C) model to measure the body composition of university club sports athletes.

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