KTH Royal Institute of Technology Reports Findings in Machine　Learning (Estimati on of aortic stiffness by finger photoplethysmography　using enhanced pulse wave analysis and machine learning)

Robotics & Machine Learning Daily News2024，Issue(Apr.10) ：19-20.

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KTH Royal Institute of Technology Reports Findings in Machine　Learning (Estimati on of aortic stiffness by finger photoplethysmography　using enhanced pulse wave analysis and machine learning)

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Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New research on Machine Learning is th e subject of a report. According to news reportingoriginating from Stockholm, S weden, by NewsRx correspondents, research stated, “Aortic stiffnessplays a crit ical role in the evolution of cardiovascular diseases, but the assessment requir es specializedequipment. Photoplethysmography (PPG) and single-lead electrocard iogram (ECG) are readily availablein healthcare and wearable devices.”

Key words

Stockholm/Sweden/Europe/Blood Pressur e/Cardiovascular Diagnostic　Techniques/Cyborgs/Diagnosis/Diagnostic Techniqu es and Procedures/Emerging Technologies/Health and Medicine/Hemodynamics/Mac hine Learning/Photoplethysmography/Plethysmogr

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出版年

2024

Robotics & Machine Learning Daily News

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KTH Royal Institute of Technology Reports Findings in Machine Learning (Estimati on of aortic stiffness by finger photoplethysmography using enhanced pulse wave analysis and machine learning)

KTH Royal Institute of Technology Reports Findings in Machine Learning (Estimati on of aortic stiffness by finger photoplethysmography using enhanced pulse wave analysis and machine learning)

Abstract

Key words

引用本文复制引用

出版年

Robotics & Machine Learning Daily News

KTH Royal Institute of Technology Reports Findings in Machine　Learning (Estimati on of aortic stiffness by finger photoplethysmography　using enhanced pulse wave analysis and machine learning)

KTH Royal Institute of Technology Reports Findings in Machine　Learning (Estimati on of aortic stiffness by finger photoplethysmography　using enhanced pulse wave analysis and machine learning)