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Erick A Perez Alday
Researcher at Emory University
Publications - 4
Citations - 245
Erick A Perez Alday is an academic researcher from Emory University. The author has contributed to research in topics: Actigraphy & Population. The author has an hindex of 3, co-authored 4 publications receiving 103 citations.
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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.
Posted ContentDOI
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 Challenge provided several innovations, including a novel evaluation metric that considers different misclassification errors for different cardiac abnormalities, reflecting the clinical reality that some diagnoses have similar outcomes and varying risks.
Proceedings ArticleDOI
Classification of 12-lead ECGs: the PhysioNet/Computing in Cardiology Challenge 2020
Matthew A. Reyna,Erick A Perez Alday,Annie Gu,Chengyu Liu,Salman Seyedi,Ali Bahrami Rad,Andoni Elola,Qiao Li,Ashish Sharma,Gari D. Clifford +9 more
TL;DR: The PhysioNet/Computing in Cardiology Challenge 2020 as mentioned in this paper focused on the identification of cardiac abnormalities in 12-lead electrocardiogram (ECG) recordings, which encouraged the development of generalizable, reproducible, and clinically relevant algorithms.
Journal ArticleDOI
Classification and Prediction of Post-Trauma Outcomes Related to PTSD Using Circadian Rhythm Changes Measured via Wrist-Worn Research Watch in a Large Longitudinal Cohort
Ayse S. Cakmak,Erick A Perez Alday,Giulia Da Poian,Ali Bahrami Rad,Thomas J. Metzler,Thomas C. Neylan,Stacey L. House,Francesca L. Beaudoin,Xinming An,Jennifer S. Stevens,Donglin Zeng,Sarah D. Linnstaedt,Tanja Jovanovic,Laura Germine,Kenneth A. Bollen,Scott L. Rauch,Christopher Lewandowski,Phyllis L. Hendry,Sophia Sheikh,Alan B. Storrow,Paul I. Musey,John P. Haran,Christopher W. Jones,Brittany E. Punches,Robert A. Swor,Nina T. Gentile,Meghan E. McGrath,Mark J. Seamon,Kamran Mohiuddin,Anna Marie Chang,Claire Pearson,Robert M. Domeier,Steven E. Bruce,Brian J. O'Neil,Niels K. Rathlev,Leon D. Sanchez,Robert H. Pietrzak,Jutta Joormann,M Deanna,Diego A. Pizzagalli,Steven E. Harte,James M. Elliott,Ronald C. Kessler,Karestan C. Koenen,Kerry J. Ressler,Samuel A. McLean,Qiao Li,Gari D. Clifford +47 more
TL;DR: In this article, the authors presented the first attempt to predict and classify post-trauma symptoms from passive wearable data using machine learning approaches that leverage the circadian desynchrony in a potential PTSD population.