no code implementations • 6 Jan 2023 • Mircea Dumitru, Qiao Li, Erick Andres Perez Alday, Ali Bahrami Rad, Gari D. Clifford, Reza Sameni
Objective: Gaussian Processes (GP)-based filters, which have been effectively used for various applications including electrocardiogram (ECG) filtering can be computationally demanding and the choice of their hyperparameters is typically ad hoc.
no code implementations • 27 Sep 2022 • Andoni Elola, Elisabete Aramendi, Jorge Oliveira, Francesco Renna, Miguel T. Coimbra, Matthew A. Reyna, Reza Sameni, Gari D. Clifford, Ali Bahrami Rad
On the test set, the algorithm achieves an unweighted average of sensitivities of 80. 4% and an F1-score of 75. 8%.
1 code implementation • Computing in Cardiology 2022 • Matthew A. Reyna, Yashar Kiarashi, Andoni Elola, Jorge Oliveira, Francesco Renna, Annie Gu, Erick A. Perez Alday, Nadi Sadr, ASHISH SHARMA, Sandra Mattos, Miguel T. Coimbra, Reza Sameni, Ali Bahrami Rad, Gari D. Clifford
Objective Cardiac auscultation is an accessible diagnostic screening tool that can help to identify patients with heart murmurs for follow-up diagnostic screening and treatment, especially in resource-constrained environments.
no code implementations • 28 Dec 2021 • Ismail Sadiq, Erick A. Perez-Alday, Amit J. Shah, Ali Bahrami Rad, Reza Sameni, Gari D. Clifford
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.
1 code implementation • Computing in Cardiology 2020 • Erick A. Perez Alday, Annie Gu, Amit Shah, Chad Robichaux, An-Kwok Ian Wong, Chengyu Liu, Feifei Liu, Ali Bahrami Rad, Andoni Elola, Salman Seyedi, Qiao Li, ASHISH SHARMA, Gari D. Clifford, Matthew A. Reyna
Main results: A total of 217 teams submitted 1395 algorithms during the Challenge, representing a diversity of approaches for identifying cardiac abnormalities from both academia and industry.
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.
no code implementations • 6 Sep 2019 • Ali Bahrami Rad, Morteza Zabihi, Zheng Zhao, Moncef Gabbouj, Aggelos K. Katsaggelos, Simo Särkkä
Results: The proposed algorithm is validated on the 2018 PhysioNet challenge dataset.
no code implementations • 1 Mar 2019 • Morteza Zabihi, Ali Bahrami Rad, Serkan Kiranyaz, Simo Särkkä, Moncef Gabbouj
Sleep arousals transition the depth of sleep to a more superficial stage.
no code implementations • 12 Dec 2018 • Zheng Zhao, Simo Särkkä, Ali Bahrami Rad
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.