TY - GEN
T1 - An ECG-Based Blood Pressure Estimation Using U-Net auto-encoder and Random Forest Regressor
AU - Aldein, Elham Alaa
AU - Abdleraheem, Mohamed
AU - Mohamed, Usama Sayed
AU - Atef, Mohamed
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Measurements of Blood Pressure (BP) have become increasingly widespread in both clinical and private settings. In parallel, Electrocardiogram (ECG) monitors have also become increasingly prevalent. However, most ECG monitors currently available do not include the ability to estimate the value of BP. To address this gap, we have devised a novel BP estimation approach that relies solely on ECG signals. Our methodology involves a series of steps, including data filtering, and segmentation, and we thoroughly investigated the potential of using the auto-encoders of U-Net neural network, as an automatic feature extractor, followed by a regression algorithm in predicting the BP from the ECG. Using the MIMIC-II dataset, the model was trained. yielded mean absolute errors (MAE) of 6.0±4.49 mmHg (MAE±STD) and 2. 5±3.7 mmHg for Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) respectively.
AB - Measurements of Blood Pressure (BP) have become increasingly widespread in both clinical and private settings. In parallel, Electrocardiogram (ECG) monitors have also become increasingly prevalent. However, most ECG monitors currently available do not include the ability to estimate the value of BP. To address this gap, we have devised a novel BP estimation approach that relies solely on ECG signals. Our methodology involves a series of steps, including data filtering, and segmentation, and we thoroughly investigated the potential of using the auto-encoders of U-Net neural network, as an automatic feature extractor, followed by a regression algorithm in predicting the BP from the ECG. Using the MIMIC-II dataset, the model was trained. yielded mean absolute errors (MAE) of 6.0±4.49 mmHg (MAE±STD) and 2. 5±3.7 mmHg for Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP) respectively.
KW - Auto-encoder
KW - Diastolic blood pressure Arterial blood pressure
KW - Electrocardiogram
KW - Systolic blood pressure
UR - http://www.scopus.com/inward/record.url?scp=85183331317&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85183331317&partnerID=8YFLogxK
U2 - 10.1109/ICM60448.2023.10378899
DO - 10.1109/ICM60448.2023.10378899
M3 - Conference contribution
AN - SCOPUS:85183331317
T3 - Proceedings of the International Conference on Microelectronics, ICM
SP - 107
EP - 112
BT - 2023 International Conference on Microelectronics, ICM 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Microelectronics, ICM 2023
Y2 - 17 November 2023 through 20 November 2023
ER -