Supplementary Material for: 'An impedance pneumography signal quality index: design, assessment and application to respiratory rate monitoring'

This supplementary material accompanies: Charlton P.H. <em>et al.</em>, "An impedance pneumography signal quality index for respiratory rate monitoring: design, assessment and application", <em>Biomedical Signal Processing and Control</em>, 65, 102339, 2021. The Impedance Pneumography Signal Quality Index (SQI) dataset and accompanying scripts (in Matlab format) are provided to facilitate reproduction of the analyses using data from the MIMIC III dataset in this publication. <strong>Summary of Publication</strong> In this article we developed and assessed the performance of a signal quality index (SQI) for the impedance pneumography signal.<br> The SQI was developed using data from the Listen dataset, and assessed using data from the Listen dataset and MIMIC III datasets.<br> The SQI was found to accurately classify segments of impedance pneumography signal as either high or low quality. Furthermore, when it was coupled with a high performance RR algorithm, highly accurate and precise RRs were estimated from those segments deemed to be high quality. In this study performance was assessed in the critical care environment - further work is required to deteremine whether the SQI is suitable for use with wearable sensors. Both the dataset and code used to perform this study are publicly available. <strong>Reproducing this Publication</strong> The work relating to the MIMIC dataset in this publication can be reproduced as follows: <strong> - Reproducing the analysis</strong><br> These steps can be used to quickly reproduce the analysis using the curated and annotated dataset. * Download the curated and annotated dataset from Zenodo using this direct download link.<br> * Run the analysis using the <em>run_imp_sqi_mimic.m</em> script. <strong> - Full reproduction</strong><br> These steps include downloading the raw data files, extracting data from these files, collating the dataset, manually annotating the data, and performing the analysis. * Use the <em>ImP_SQI_mimic_data_importer.m</em> script to download raw MIMIC data files from PhysioNet, and collate them into a single Matlab file.<br> * Prepare the dataset for manual annotation by running the <em>run_imp_sqi_mimic.m</em> script.<br> * Manually annotate the signals by running the <em>run_mimic_imp_annotation.m</em> script - the annotations are stored in separate files (the original annotation files are available here).<br> * Import the manual annotations into the collated data file by re-running the <em>ImP_SQI_mimic_data_importer.m</em> script.<br> * Run <em>run_imp_sqi_mimic.m</em> to perform the analysis described in the publication. <strong> - Submitted manuscript</strong> The submitted manuscript is available here. The scripts are also stored (alongside details of how to use them) are available in the RRest GitHub repository at: https://github.com/peterhcharlton/RRest/tree/master/RRest_v3.0/Publication_Specific_Scripts/ImP_SQI License: The dataset (<em>mimic_imp_sqi_data.mat</em>) is distributed under the terms specified in the accompanying LICENSE file. The scripts are distributed under the GNU General Public Licence (as specified towards the start of each file). Version 1.0: This version includes the submitted manuscript.