[2405.00219] Machine Learning-based Estimation of Respiratory Fluctuations in a Healthy Adult Population using BOLD fMRI and Head Motion Parameters
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Computer Science > Machine Learning

arXiv:2405.00219 (cs)
[Submitted on 30 Apr 2024]

Title:Machine Learning-based Estimation of Respiratory Fluctuations in a Healthy Adult Population using BOLD fMRI and Head Motion Parameters

Authors:Abdoljalil Addeh, Fernando Vega, Rebecca J. Williams, G. Bruce Pike, M. Ethan MacDonald
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Abstract:Motivation: In many fMRI studies, respiratory signals are often missing or of poor quality. Therefore, it could be highly beneficial to have a tool to extract respiratory variation (RV) waveforms directly from fMRI data without the need for peripheral recording devices.
Goal(s): Investigate the hypothesis that head motion parameters contain valuable information regarding respiratory patter, which can help machine learning algorithms estimate the RV waveform.
Approach: This study proposes a CNN model for reconstruction of RV waveforms using head motion parameters and BOLD signals.
Results: This study showed that combining head motion parameters with BOLD signals enhances RV waveform estimation.
Impact: It is expected that application of the proposed method will lower the cost of fMRI studies, reduce complexity, and decrease the burden on participants as they will not be required to wear a respiratory bellows.
Comments: 6 pages, 5 figure, conference abstract
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2405.00219 [cs.LG]
  (or arXiv:2405.00219v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2405.00219

Submission history

From: Abdoljalil Addeh [view email]
[v1] Tue, 30 Apr 2024 21:53:11 UTC (2,563 KB)
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