A Shallow U-Net Architecture for Reliably Predicting Blood Pressure (BP) from Photoplethysmogram (PPG) and Electrocardiogram (ECG) Signals

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 3; p. 919
• Main Authors: Mahmud, Sakib, Ibtehaz, Nabil, Khandakar, Amith, Tahir, Anas M, Rahman, Tawsifur, Islam, Khandaker Reajul, Hossain, Md Shafayet, Rahman, M Sohel, Musharavati, Farayi, Ayari, Mohamed Arselene, Islam, Mohammad Tariqul, Chowdhury, Muhammad E H
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 25.01.2022; MDPI
• Subjects: arterial blood pressure; autoencoder; Blood Pressure; Blood Pressure Determination; Calibration; Datasets; Deep learning; diastolic blood pressure; electrocardiogram; Electrocardiography; Heart; Humans; Hypertension; Hypertension - diagnosis; Intensive care; Machine learning; Monitoring; Neural networks; photoplethysmogram; Photoplethysmography; Signal processing; systolic blood pressure; Telemedicine; electrocardiogram; arterial blood pressure; autoencoder; diastolic blood pressure; feature extraction; photoplethysmogram; systolic blood pressure

Cardiovascular diseases are the most common causes of death around the world. To detect and treat heart-related diseases, continuous blood pressure (BP) monitoring along with many other parameters are required. Several invasive and non-invasive methods have been developed for this purpose. Most exis...