Summary of Machine Learning-based Estimation Of Respiratory Fluctuations in a Healthy Adult Population Using Bold Fmri and Head Motion Parameters, by Abdoljalil Addeh et al.
Machine Learning-based Estimation of Respiratory Fluctuations in a Healthy Adult Population using BOLD fMRI and Head Motion Parameters
by Abdoljalil Addeh, Fernando Vega, Rebecca J. Williams, G. Bruce Pike, M. Ethan MacDonald
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposes a method to extract respiratory variation (RV) waveforms from functional magnetic resonance imaging (fMRI) data, addressing a common issue in many fMRI studies where respiratory signals are often missing or of poor quality. By leveraging machine learning techniques, the authors aim to develop a tool that can accurately identify and quantify RV patterns directly from fMRI data, eliminating the need for peripheral recording devices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a way to find breathing pattern changes in brain scans without needing special equipment. Usually, these patterns are hard to get or missing, which makes it hard for scientists to study how our brains work while we breathe. The researchers want to fix this problem by using special computer tools to find the breathing patterns right in the brain scan images. |
Keywords
» Artificial intelligence » Machine learning
