CardioPhi's technology is a product that ties in medical research with the latest technological advancements in the market. Here is the research particularly by our team members.
ECG BERT: Understanding Hidden Language of ECGs with Self-Supervised
Representation Learning
Choi, Seokmin, Sajad Mousavi, Phillip Si, Haben G. Yhdego, Fatemeh Khadem, and Fatemeh Afghah. arXiv preprint arXiv:2306.06340 (2023)
View the PaperHAN-ECG: An Interpretable Atrial Fibrillation Detection Model Using Hierarchical Attention Networks
S. Mousavi, et al, Computers in Biology and Medicine 127 (2020): 104057
View the PaperECG Language processing (ELP): A new technique to analyze ECG signals
S. Mousavi, et al, Computer methods and programs in biomedicine (2021)
View the PaperSingle-modal and multi-modal false arrhythmia alarm reduction using attention-based convolutional and recurrent neural networks
S. Mousavi, et al, Plos One (2020)
View the PaperInter-andIntra-Patient ECG Heart beat Classification For Arrhythmia Detection: A Sequence to Sequence Deep Learning Approach
S. Mousavi, et al, IEEE ICASSP (2019)
View the PaperECGNET: Learning Where to Attend for Detection of
Atrial Fibrillation with Deep Visual Attention
S. Mousavi, et al, IEEE BHI (2019)
View the Paper