Saad Abdullah, Associated Senior Lecturer
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. 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.
Enhancing Speech Emotion Recognition Using Deep Convolutional Neural Networks (May 2024) M M Manjurul Islam , Md Alamgir Kabir , Alamin Sheikh , Muhammad Saiduzzaman , Abdelakram Hafid, Saad Abdullah International Conference on Machine Learning Technologies (ICMLT)
Impact of Activities in Daily Living on Electrical Bioimpedance Measurements for Bladder Monitoring (Jul 2023) Abdelakram Hafid, Saad Abdullah, Maria Lindén, Annica Kristoffersson, Mia Folke 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS) (CBMS'23)
Machine Learning-Based Classification of Hypertension using CnD Features from Acceleration Photoplethysmography and Clinical Parameters (Jul 2023) Saad Abdullah, Abdelakram Hafid, Maria Lindén, Mia Folke, Annica Kristoffersson 2023 IEEE 36th International Symposium on Computer-Based Medical Systems (CBMS) (CBMS'23)
PPGFeat: a novel MATLAB toolbox for extracting PPG fiducial points (Jun 2023) Saad Abdullah, Abdelakram Hafid, Mia Folke, Maria Lindén, Annica Kristoffersson
State of the Art of Non-Invasive Technologies for Bladder Monitoring: A Scoping Review (Mar 2023) Abdelakram Hafid, Sabrina Difallah , Camilla Alves , Saad Abdullah, Mia Folke, Maria Lindén, Annica Kristoffersson Sensors (MDPI Sensors)
A Novel Fiducial Point Extraction Algorithm to Detect C and D Points from the Acceleration Photoplethysmogram (CnD) (Feb 2023) Saad Abdullah, Abdelakram Hafid, Mia Folke, Maria Lindén, Annica Kristoffersson Electronics (MDPI Electronics)