The field of Electrocardiogram (EKG/ECG) analysis has seen significant advancements with the advent of deep learning models. State-of-the-art models such as ResNet, Transformer, ViT, and H3 have demonstrated remarkable performance in EKG classification tasks. However, the quest for more efficient and accurate models is a continuous journey. This proposal introduces a novel project, provisionally named Mamba Biometric EKG Analysis Technology (MambaBEAT), which aims to leverage the Mamba model, a recent and promising sequence modeling architecture, for EKG analysis.
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In the realm of EKG/ECG analysis, deep learning models have made significant strides. However, the pursuit of efficiency and accuracy persists. The proposed Mamba Biometric EKG Analysis Technology (MambaBEAT) project aims to utilize the Mamba model, a promising sequence modeling architecture, to further advance EKG analysis.
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SeroviICAI/Mamba-Biometric-EKG-Analysis-Technology-MambaBEAT
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In the realm of EKG/ECG analysis, deep learning models have made significant strides. However, the pursuit of efficiency and accuracy persists. The proposed Mamba Biometric EKG Analysis Technology (MambaBEAT) project aims to utilize the Mamba model, a promising sequence modeling architecture, to further advance EKG analysis.
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