1 code implementation • 24 Jul 2020 • Lida Zhang, Nathan C. Hurley, Bassem Ibrahim, Erica Spatz, Harlan M. Krumholz, Roozbeh Jafari, Bobak J. Mortazavi
A practical problem with this approach is that the amount of data required to confidently train such a regression model can be prohibitive.
1 code implementation • 1 Jul 2021 • Asiful Arefeen, Ali Akbari, Seyed Iman Mirzadeh, Roozbeh Jafari, Behrooz A. Shirazi, Hassan Ghasemzadeh
However, extracting IBIs from noisy signals is challenging since the morphology of the signal is distorted in the presence of the noise.
no code implementations • 12 Aug 2019 • Nathan C. Hurley, Erica S. Spatz, Harlan M. Krumholz, Roozbeh Jafari, Bobak J. Mortazavi
We highlight three primary needs in the design of new smart health technologies: 1) the need for sensing technology that can track longitudinal trends in signs and symptoms of the cardiovascular disorder despite potentially infrequent, noisy, or missing data measurements; 2) the need for new analytic techniques that model data captured in a longitudinal, continual fashion to aid in the development of new risk prediction techniques and in tracking disease progression; and 3) the need for machine learning techniques that are personalized and interpretable, allowing for advancements in shared clinical decision making.
no code implementations • 11 Jan 2022 • Jonathan Martinez, Kaan Sel, Bobak J. Mortazavi, Roozbeh Jafari
Goal: To achieve-high quality comprehensive feature extraction from physiological signals that enables precise physiological parameter estimation despite evolving waveform morphologies.