A
Ali Bahrami Rad
Researcher at Emory University
Publications - 35
Citations - 872
Ali Bahrami Rad is an academic researcher from Emory University. The author has contributed to research in topics: Pulseless electrical activity & Convolutional neural network. The author has an hindex of 10, co-authored 32 publications receiving 535 citations. Previous affiliations of Ali Bahrami Rad include Helsinki University of Technology & University of Tampere.
Papers
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Journal ArticleDOI
Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020.
Erick A Perez Alday,Annie Gu,Amit J. Shah,Chad Robichaux,An-Kwok Ian Wong,Chengyu Liu,Feifei Liu,Ali Bahrami Rad,Andoni Elola,Andoni Elola,Salman Seyedi,Qiao Li,Ashish Sharma,Gari D. Clifford,Gari D. Clifford,Matthew A. Reyna +15 more
TL;DR: This work addresses issues by providing a standard, multi-institutional database and a novel scoring metric through a public competition: the PhysioNet/Computing in Cardiology Challenge 2020, setting a new bar in reproducibility for public data science competitions.
Proceedings ArticleDOI
Heart sound anomaly and quality detection using ensemble of neural networks without segmentation
TL;DR: The objective of this study is to develop an automatic classification method for anomaly and quality detection of PCG recordings without segmentation in order to evaluate the heart hemodynamic status and to detect a cardiovascular disease.
Proceedings ArticleDOI
Detection of atrial fibrillation in ECG hand-held devices using a random forest classifier
Morteza Zabihi,Ali Bahrami Rad,Aggelos K. Katsaggelos,Serkan Kiranyaz,Susanna Narkilahti,Moncef Gabbouj +5 more
TL;DR: This study is focused on detection of AF rhythm using hand-held ECG monitoring devices, in addition to three other classes: normal or sinus rhythm, other rhythms, and too noisy to analyze.
Journal ArticleDOI
Analysis of High-Dimensional Phase Space via Poincaré Section for Patient-Specific Seizure Detection
Morteza Zabihi,Serkan Kiranyaz,Ali Bahrami Rad,Aggelos K. Katsaggelos,Moncef Gabbouj,Turker Ince +5 more
TL;DR: A novel patient-specific seizure detection approach is proposed based on the dynamics of EEG signals and the proposed approach achieved 88.27% sensitivity and 93.21% specificity on average with 25% training data.
Journal ArticleDOI
ECG-Based Classification of Resuscitation Cardiac Rhythms for Retrospective Data Analysis
Ali Bahrami Rad,Trygve Eftestøl,Kjersti Engan,Unai Irusta,Jan Terje Kvaløy,Jo Kramer-Johansen,Lars Wik,Aggelos K. Katsaggelos +7 more
TL;DR: The results demonstrate that it is possible to classify resuscitation cardiac rhythms automatically, but the accuracy for the organized rhythms (PEA and PR) is low.