Ismail Elnaggar
M.Sc. (Tech)
ismail.m.elnaggar@utu.fi |
Biomedical Engineering; Health Technology; Medical Data Analytics; Bio-signal Processing;
NewLife Project, Moore4Medical Project
B.S. in Electrical and Computer Engineering from The Ohio State University.
M.Sc. (Tech) in Medical Analytics and Health IoT from the University of Turku.
Currently working as a project researcher and PhD researcher at the Digital Health Technology Group located at the University of Turku.
My research centers on non-invasive wearable heart disease monitoring and detection systems. The focus of my work consists of investigating seismocardiography (SCG), gyrocardiography (GCG), and ballistocardiography (BCG) to better understand how these bio signals can be used in future technologies that may aid clinicians in detecting and monitoring different kinds of heart disease.
Teaching Assistant for:
DTEK0042 Acquistion and Analysis of Biosginals
DTEK0086 Biosignal Analytics
DTEK2062 Lääketieteellisen tekniikan ja terveysteknologian laboratoriotyöt
DTEK2095 Analytics and Programming for Health Wearables
- Bed sensor ballistocardiogram for non-invasive detection of atrial fibrillation: a comprehensive clinical study (2025)
- Physiological Measurement
- Continuous Radar-based Heart Rate Monitoring using Autocorrelation-based Algorithm in Intensive Care Unit (2025)
- IEEE Journal of Biomedical and Health Informatics
- Validation of an MEMS-Based Pressure Sensor System for Atrial Fibrillation Detection from Wrist and Finger (2025)
- IEEE Sensors Journal
- Evaluating Piezoelectric Ballistocardiography for Post-Surgical Heart Rate Monitoring (2024)
- Computing in Cardiology
- Generating Synthetic Mechanocardiograms for Machine Learning Based Peak Detection (2024)
- IEEE Sensors Letters
- Cardiac Time Intervals Derived from Electrocardiography and Seismocardiography in Different Patient Groups (2022)
- Computing in Cardiology
- Multichannel Bed Based Ballistocardiography Heart Rate Estimation Using Continuous Wavelet Transforms and Autocorrelation (2022)
- Computing in Cardiology
- Detecting Aortic Stenosis Using Seismocardiography and Gryocardiography Combined with Convolutional Neural Networks (2021)
- Computing in Cardiology
