3260 papers • 126 benchmarks • 313 datasets
Detection of hypertension (high blood pressure) using vital signals, such as ECG, PPG, etc.
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A multimodal deep learning system, dubbed HyMNet, which combines fundus images and cardiometabolic risk factors, specifically age and gender, to improve hypertension detection capabilities is introduced, concluding that diabetes is used as a confounding variable for distinguishing hypertensive cases.
The results show that this relationship extends beyond heart rate and blood pressure, demonstrating the feasibility of hypertension detection with generalization, and the utilized transform using convolution kernels outperforms the methods proposed in the previous studies and state-of-the-art deep learning models.
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