TY - GEN
T1 - Blood pressure prediction from photoplethysmogram signal using artificial intelligence
AU - Shinde, Rutuja M.
AU - Manga, Sharanya
AU - Muthavarapu, Neha
AU - Gopalakrishnan, Keerthy
AU - Aakre, Christopher A.
AU - Ryu, Alexander J.
AU - Arunachalam, Shivaram P.
N1 - Publisher Copyright:
© 2023 by ASME.
PY - 2023
Y1 - 2023
N2 - Blood pressure measurement in current medical practice relies on manual methods with the most widely used modality being sphygmomanometers. Utilizing the principle of Photoplethysmography, it is possible to provide an accurate reading of one s blood pressure through light signals and photodetector devices. This research paper introduces a new Artificial Intelligence driven approach to predict Blood pressure levels and classify them according to the updated ACC (American College of Cardiology) criteria as Normal, Elevated, Stage I, and II Hypertension from the given PPG signal values using Machine Learning Models. This research paper aims to accurately read the Systolic and Diastolic Blood Pressure using Artificial Intelligence, place them into the correct value bins and further prove that the blood pressure values differ based on different skin tones in different light wavelengths such as red, infrared, and green. Machine Learning models such as the Support Vector Machine have shown an accuracy of 70.58% for Systolic Blood Pressure and Decision Tree with an accuracy of 74.4% for Diastolic Blood Pressure classification. The models used in this research are Support Vector Machine, Decision Tree and K-Nearest Neighbor. This research study has future applications and extensions to predict blood pressure levels for patients with different skin tones under different light radiations and PPG signal readings. Neural Network models will be developed to compare the blood predictions from this work.
AB - Blood pressure measurement in current medical practice relies on manual methods with the most widely used modality being sphygmomanometers. Utilizing the principle of Photoplethysmography, it is possible to provide an accurate reading of one s blood pressure through light signals and photodetector devices. This research paper introduces a new Artificial Intelligence driven approach to predict Blood pressure levels and classify them according to the updated ACC (American College of Cardiology) criteria as Normal, Elevated, Stage I, and II Hypertension from the given PPG signal values using Machine Learning Models. This research paper aims to accurately read the Systolic and Diastolic Blood Pressure using Artificial Intelligence, place them into the correct value bins and further prove that the blood pressure values differ based on different skin tones in different light wavelengths such as red, infrared, and green. Machine Learning models such as the Support Vector Machine have shown an accuracy of 70.58% for Systolic Blood Pressure and Decision Tree with an accuracy of 74.4% for Diastolic Blood Pressure classification. The models used in this research are Support Vector Machine, Decision Tree and K-Nearest Neighbor. This research study has future applications and extensions to predict blood pressure levels for patients with different skin tones under different light radiations and PPG signal readings. Neural Network models will be developed to compare the blood predictions from this work.
KW - Artificial Intelligence
KW - Blood Pressure
KW - Diastolic Blood Pressure (DBP)
KW - Hypertension
KW - Machine Learning
KW - Photoplethysmography (PPG) Signal
KW - Systolic Blood Pressure (SBP)
KW - Time Series data
UR - http://www.scopus.com/inward/record.url?scp=85165010008&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85165010008&partnerID=8YFLogxK
U2 - 10.1115/DMD2023-2769
DO - 10.1115/DMD2023-2769
M3 - Conference contribution
AN - SCOPUS:85165010008
T3 - Proceedings of the 2023 Design of Medical Devices Conference, DMD 2023
BT - Proceedings of the 2023 Design of Medical Devices Conference, DMD 2023
PB - American Society of Mechanical Engineers
T2 - 2023 Design of Medical Devices Conference, DMD 2023
Y2 - 17 April 2023 through 21 April 2023
ER -