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Lecturer

Ismail Mohamed Ibrahim

Assistant Professor

biography

Ismail M. El-Badawy received the B.Sc and M.Sc degrees in Electronics and Communication Engineering from the Arab Academy for Science and Technology and Maritime Transport, Cairo, Egypt, in 2009 and 2014, respectively. He received the Ph.D degree in Electrical Engineering from Universiti Teknologi Malaysia (UTM) in 2023. He is currently an Assistant Professor at the Electronics and Communications Engineering Department, Arab Academy for Science and Technology and Maritime Transport, Cairo, Egypt. His research interests include Biomedical signal processing, Genomic signal processing and Bioinformatics. He is a member of Asia-Pacific Signal and Information Processing Association (APSIPA). He participated in a number of IEEE conferences, such as IEEE Global Conference on Signal and Information Processing (GlobalSIP) and IEEE International Conference on Signal and Image Processing Applications (ICSIPA). He has authored and co-authored different conference and journal papers which are indexed in Scopus and Web of Science.

Education

2023

Doctorate - Universiti Teknologi Malaysia (UTM) - Malaysia

Doctor of Philosophy (PhD) in Biomedical Signal Processing

2014

Master - Arab Academy for Science and Technology - Cairo - Egypt

Master Degree of Electronics and Communications Engineering

2009

Bachelor - Arab Academy for Science & Technology - Cairo -

Bachelor Degree of Electronics & Communications Engineering

experience

work experience

2023 - Till Now

Assistant Professor

Electronics & Communication Engineering-cairo

2014 - 2023

Assistant Lecturer

Electronics and Communications Engineering Dept.

2009 - 2014

Graduate Teaching Assistant

Electronics and Communications Engineering Dept.

academic experience

publications

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Honors & awards

Honors

Best Paper Award - IEEE 8th International Conference on Signal and Image Processing Applications (IEEE ICSIPA 2024)

A certificate of award is presented in recognition of the paper entitled "ECG Baseline Wander Reduction Using Empirical Mode Decomposition: A Case Study on Preterm Infants with Bradycardia" Being selected for BEST PAPER AWARD at The IEEE 8th International Conference on Signal and Image Processing Applications (IEEE ICSIPA 2024), 3 - 5 September 2024, Kuala Lumpur, Malaysia.

Professional Experience

communities

Teaching Courses

Course Academic year Term

CCS2102 - Digital Logic Design

2024 Fall Semester View All Content

EC534 - Analog & Digital Signal Processing

2024 Spring Semester View All Content

EC217 - Measurements & Instrumentation

2024 Spring Semester View All Content

projects

Detection of bradycardia in preterm infants using ECG and respiratory signals

Graduation Project
Start Date : 01 Oct 2023-01 Jul 2024
Cardiorespiratory dysregulation in newborn infants is a main concern that influences the infant’s hospital discharge. Therefore, it is common practice to delay discharge by five consecutive days free of any cardiorespiratory events in order to avoid sudden unexplained life-threatening events after discharge, as such occurrence may result in re-hospitalization. Bradycardia is one of these cardiorespiratory events that is common in preterm infants and may occur spontaneously for several reasons. Bradycardia is defined as a heart condition, where the heart beats at a slower rate than normal. Automatic detection of such a serious health condition is expected to help achieve improved diagnostic and focused management strategies for preterm-born neonates. Hence, this project aims to present a signal processing-based approach for detecting bradycardia in preterm infants. For this, a dataset comprising electrocardiogram (ECG) and respiratory signals recorded from a number of preterm infants admitted to the hospital’s neonatal intensive care unit (NICU) will be used. MATLAB software will be utilized as a simulation tool to incorporate different signal processing techniques with the purpose of analyzing the recorded ECG and respiratory signals. The principal role of employing these signal processing techniques is to extract appropriate features from the collected physiological signals, which will be subsequently used to train a classification model. The capability of this classification model in distinguishing infants with bradycardia from the control group will be tested in terms of different classification performance parameters. Moreover, we look forward to coming up with a hardware prototype of the model that will be developed during the project according to the time plan and the progress of the participating students.

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Branch : AASTMT Heliopolis Branch, Cairo, Egypt

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Location : Heliopolis B 310