Objective: To determine if a realistic, but computationally efficient model of the electrocardiogram can be used to pre-train a deep neural network (DNN) with a wide range of morphologies and abnormalities specific to a given condition - T-wave Alternans (TWA) as a result of Post-Traumatic Stress Disorder, or PTSD - and significantly boost performance on a small database of rare individuals.
no code implementations • 2 Aug 2021 • Jorge Oliveira, Francesco Renna, Paulo Dias Costa, Marcelo Nogueira, Cristina Oliveira, Carlos Ferreira, Alipio Jorge, Sandra Mattos, Thamine Hatem, Thiago Tavares, Andoni Elola, Ali Bahrami Rad, Reza Sameni, Gari D Clifford, Miguel T. Coimbra
This is mainly due to the lack of large publicly available datasets, where a more detailed description of such abnormal waves (e. g., cardiac murmurs) exists.
no code implementations • 14 Nov 2020 • Ayse S. Cakmak, Nina Thigpen, Garrett Honke, Erick Perez Alday, Ali Bahrami Rad, Rebecca Adaimi, Chia Jung Chang, Qiao Li, Pramod Gupta, Thomas Neylan, Samuel A. McLean, Gari D. Clifford
The results indicate that the VAE model is a promising approach for actigraphy data analysis for mental health outcomes in long-term studies.
Results: The proposed algorithm is validated on the 2018 PhysioNet challenge dataset.
Sleep arousals transition the depth of sleep to a more superficial stage.
In this article, we propose a novel ECG classification framework for atrial fibrillation (AF) detection using spectro-temporal representation (i. e., time varying spectrum) and deep convolutional networks.