
This study demonstrates the effectiveness of using pretrained masked language models with textual representations for interpreting intracardiac electrograms, achieving impressive results in atrial fibrillation classification and signal interpolation.


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leveraging-pretrained-language-models-to-interpret-intracardiac-electrograms-for-atrial-fibrillation-detection


Leveraging Pretrained Language Models to Interpret Intracardiac Electrograms for Atrial Fibrillation Detection


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A novel interpretable and efficient architecture for medical time series processing that achieves performance similar to state-of-the-art deep neural networks with several orders of magnitude fewer parameters.


sparse-mixture-of-learned-kernels-for-interpretable-and-efficient-medical-time-series-processing


Sparse Mixture of Learned Kernels for Interpretable and Efficient Medical Time Series Processing
