Saad Abdullah, Post Doc

Saad Abdullah is a dynamic researcher with a Ph.D. in Biomedical Engineering. He is driven by a passion to revolutionize healthcare through technological advancements. With expertise in medical devices, biosensors, and signal processing, Saad specializes in non-invasive monitoring and digital health solutions. He is skilled in laboratory operations, teaching, and effective scientific communication. Saad is particularly passionate about integrating photoplethysmography (PPG), machine learning, and artificial intelligence (AI) to deliver transformative outcomes in patient care. Saad earned his Ph.D. in Biomedical/Medical Engineering from Università degli Studi di Brescia, where he was actively involved in the design and development of medical equipment. His major project involved designing and developing advanced measurement systems for biomolecule detection. Saad also spearheaded an innovative approach for the detection of p53 protein using spectrophotometry and designed a cutting-edge 3-axis magnetoresistive sensor for the detection of Earth’s magnetic field. His exceptional work earned him the prestigious Erasmus+ scholarship for conducting Ph.D. research at the University Polytechnique Catalunya (UPC) in Spain.

Saad Abdullah specializes in non-invasive monitoring and digital health solutions, with a particular focus on Biosignals. Saad is passionate about integrating PPG, machine learning, and artificial intelligence (AI) to deliver transformative outcomes in patient care. His expertise in signal processing and AI-based models enables him to analyze PPG signals for cardiovascular health monitoring. Saad’s research aims to advance the field of PPG signal analysis and develop innovative techniques for accurate and real-time cardiovascular health assessment.

Saad has made significant contributions to the field of PPG signal analysis. One of his notable publications is titled "A Novel Fiducial Point Extraction Algorithm to Detect C and D Points from the Acceleration Photoplethysmogram (CnD)". In this study, Saad and his colleagues proposed a novel fiducial point extraction algorithm that accurately identifies c and d points from the acceleration photoplethysmogram (APG). The algorithm demonstrated a high level of accuracy in detecting fiducial points, making it a valuable resource for researchers and healthcare professionals working with photoplethysmography signals.