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A Medical Comparative Study Evaluating Electrocardiogram Signal-Based Blood Pressure Estimation
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Author(s): Siham Moussaoui (Department of Electrical Engineering Systems, Systems and Telecommunications Engineering Laboratory, Boumerdes University, Algeria), Sid Ali Fellag (Boumerdes University, Algeria)and Hocine Chebi (Faculty of Electrical Engineering, Laboratory Intelligent Control and Electrical Power System (ICEPS), Djillali Liabes University of Sidi Bel Abbes, Algeria)
Copyright: 2024
Pages: 7
Source title:
Future of AI in Medical Imaging
Source Author(s)/Editor(s): Avinash Kumar Sharma (Sharda University, India), Nitin Chanderwal (University of Cincinnati, USA), Shobhit Tyagi (Sharda University, India)and Prashant Upadhyay (Sharda University, India)
DOI: 10.4018/979-8-3693-2359-5.ch004
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Abstract
In general, blood pressure (BP) is measured using standard methods (medical monitors), which are widely used, or from physiological sensor data, which is a difficult task usually solved by combining several signals. In recent research, electrocardiogram (ECG) signals alone have been used to estimate blood pressure. The authors present a comparative study that evaluates ECG signal-based blood pressure estimation using complexity analysis to extract features, comparing the results obtained with a random forest regression model as well as with the combination of a stacking-based classification module and a regression module. It was determined that the best result obtained is a mean absolute error range of 3.73 mmHg with a standard deviation of 5.19 mmHg for diastolic blood pressure (DBP) and 5.92 mmHg with a standard deviation of 7.23 mmHg for systolic blood pressure (PAS).
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