Summary of Inn-par: Invertible Neural Network For Ppg to Abp Reconstruction, by Soumitra Kundu and Gargi Panda and Saumik Bhattacharya and Aurobinda Routray and Rajlakshmi Guha
INN-PAR: Invertible Neural Network for PPG to ABP Reconstruction
by Soumitra Kundu, Gargi Panda, Saumik Bhattacharya, Aurobinda Routray, Rajlakshmi Guha
First submitted to arxiv on: 13 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces an invertible neural network (INN) for photoplethysmography (PPG) to arterial blood pressure (ABP) reconstruction, aiming to improve the accuracy of non-invasive and continuous BP monitoring. The proposed INN-PAR model employs a series of invertible blocks to jointly learn the mapping between PPG and its gradient with the ABP signal and its gradient. This approach efficiently captures both forward and inverse mappings simultaneously, preventing information loss. Additionally, the model incorporates a multi-scale convolution module (MSCM) within the invertible block to effectively learn features across multiple scales. Experimental results on two benchmark datasets demonstrate that INN-PAR outperforms state-of-the-art methods in waveform reconstruction and BP measurement accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to measure blood pressure using light sensors. It uses special computers called neural networks to improve the accuracy of this method. The network is designed to learn the relationship between light signals and blood pressure, and it’s able to capture important details that were missing in previous methods. This can help doctors detect cardiovascular diseases earlier, which could save lives. |
Keywords
» Artificial intelligence » Neural network
