A product backed by research.

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)

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HAN-ECG: An Interpretable Atrial Fibrillation Detection Model Using Hierarchical Attention Networks

S. Mousavi, et al, Computers in Biology and Medicine 127 (2020): 104057

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ECG Language processing (ELP): A new technique to analyze ECG signals

S. Mousavi, et al,  Computer methods and programs in biomedicine (2021)

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Single-modal and multi-modal false arrhythmia alarm reduction using attention-based convolutional and recurrent neural networks

S. Mousavi, et al,  Plos One (2020)

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Inter-andIntra-Patient ECG Heart beat  Classification For Arrhythmia Detection: A Sequence to Sequence Deep Learning Approach

S. Mousavi, et al, IEEE ICASSP (2019)

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ECGNET: Learning Where to Attend for Detection of
Atrial Fibrillation with Deep Visual Attention

S. Mousavi, et al, IEEE BHI (2019)

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