Using data collected from structured light plethysmography to differentiate breathing pattern disorder from normal breathing: A study group report
Bindi S. Brook, Mathew Bulpett, Robin Curnow, Emily Fraser, Eric J., Hall, Shiting Huang, Mariam Mubarak, Carl A. Whitfield

TL;DR
This study investigates the potential of structured light plethysmography (SLP) data to distinguish breathing pattern disorder from normal breathing in post-COVID patients, exploring various analytical approaches without definitive biomarkers.
Contribution
It evaluates multiple data analysis methods on SLP data from healthy and BPD patients, providing insights and recommendations for future research.
Findings
No clear SLP biomarkers identified for BPD
Explored dimensionality reduction and index extraction techniques
Proposed future directions including spatial indices and mechanical modeling
Abstract
This report relates to a study group hosted by the EPSRC funded network, Integrating data-driven BIOphysical models into REspiratory MEdicine (BIOREME), and supported by SofTMech and Innovate UK, Business Connect. This report summarises the work undertaken on a challenge presented by two of the authors, Mathew Bulpett and Dr Emily Fraser. The aim was to identify approaches to analyse data collected using structured light plethysmography (SLP) from (n=31) healthy volunteers and (n=67) patients with Breathing Pattern Disorder (BPD) attributed to "long COVID", i.e. post-acute COVID-19 sequelae. This report explores several approaches including dimensionality reduction techniques on the available data and alternative indices extracted from variation in the time-series data for each measurement. Further proposals are also outlined such as different spatial indices that could be extracted…
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Taxonomy
TopicsNon-Invasive Vital Sign Monitoring · Advanced Chemical Sensor Technologies · Infrared Thermography in Medicine
