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5 results for “index signal”
Supplementary Material for: 'An impedance pneumography signal quality index: design, assessment and application to respiratory rate monitoring'
<p>This supplementary material accompanies:</p> <p>Charlton P.H. <em>et al.</em>, "<a href="https://doi.org/10.1016/j.bspc.2020.102339">An impedance pneumography signal quality index for respiratory rate monitoring: design, assessment and application</a>", <em>Biomedical Signal Processing and Control</em>, 65, 102339, 2021.</p> <p>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.</p> <p><strong>Summary of Publication</strong></p> <p>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 <a href="http://peterhcharlton.github.io/RRest/listen_dataset.html">Listen dataset</a>, and assessed using data from the <a href="http://peterhcharlton.github.io/RRest/listen_dataset.html">Listen dataset</a> 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.</p> <p><strong>Reproducing this Publication</strong></p> <p>The work relating to the MIMIC dataset in this publication can be reproduced as follows:</p> <p><strong> - Reproducing the analysis</strong><br> These steps can be used to quickly reproduce the analysis using the curated and annotated dataset.</p> <p>* Download the curated and annotated dataset from <a href="https://doi.org/10.5281/zenodo.3973770">Zenodo</a> using this <a href="https://zenodo.org/record/3973771/files/mimic_imp_sqi_data.mat?download=1">direct download link</a>.<br> * Run the analysis using the <a href="https://zenodo.org/record/3973771/files/run_imp_sqi_mimic.m?download=1"><em>run_imp_sqi_mimic.m</em></a> script.</p> <p><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.</p> <p>* Use the <a href="https://zenodo.org/record/3973771/files/ImP_SQI_mimic_data_importer.m?download=1"><em>ImP_SQI_mimic_data_importer.m</em></a> 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 <a href="https://zenodo.org/record/3973771/files/run_imp_sqi_mimic.m?download=1"><em>run_imp_sqi_mimic.m</em></a> script.<br> * Manually annotate the signals by running the <a href="https://zenodo.org/record/3973771/files/run_imp_sqi_mimic.m?download=1"><em>run_mimic_imp_annotation.m</em></a> script - the annotations are stored in separate files (the original annotation files are available <a href="https://zenodo.org/record/3974113/files/2019_annotations.zip?download=1">here</a>).<br> * Import the manual annotations into the collated data file by re-running the <a href="https://zenodo.org/record/3973771/files/ImP_SQI_mimic_data_importer.m?download=1"><em>ImP_SQI_mimic_data_importer.m</em></a> script.<br> * Run <a href="https://zenodo.org/record/3973771/files/run_imp_sqi_mimic.m?download=1"><em>run_imp_sqi_mimic.m</em></a> to perform the analysis described in the publication.</p> <p><strong> - Submitted manuscript</strong></p> <p>The submitted manuscript is available <a href="https://zenodo.org/record/5211463/files/Impedance%20SQI%20manuscript%20-%20Oct%202020%20revision.docx?download=1">here</a>.</p> <p>The scripts are also stored (alongside details of how to use them) are available in the <a href="http://peterhcharlton.github.io/RRest/">RRest GitHub repository</a> at: <a href="https://github.com/peterhcharlton/RRest/tree/master/RRest_v3.0/Publication_Specific_Scripts/ImP_SQI">https://github.com/peterhcharlton/RRest/tree/master/RRest_v3.0/Publication_Specific_Scripts/ImP_SQI</a></p> <p>License: The dataset (<a href="https://zenodo.org/record/3974113/files/mimic_imp_sqi_data.mat?download=1"><em>mimic_imp_sqi_data.mat</em></a>) 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).</p> <p>Version 1.0: This version includes the submitted manuscript.</p> <p> </p>
TCF7L2 lncRNA: A Link between Bipolar Disorder and Body Mass Index through Glucocorticoid Signaling [RNA-Seq]
GEO Series GSE179921. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
TCF7L2 lncRNA: A Link between Bipolar Disorder and Body Mass Index through Glucocorticoid Signaling
GEO Series GSE179922. Homo sapiens. 10 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
TCF7L2 lncRNA: A Link between Bipolar Disorder and Body Mass Index through Glucocorticoid Signaling [ChIP-Seq]
GEO Series GSE179762. Homo sapiens. 2 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Refractive index determination of dynamic droplets in a flow by analyzing light scattering signals with a machine learning approach
<p>This container includes the measurement data, python script and weights of trained machine learning model associated with the scientific work, which will be presented in 2025 at the <em><strong>Turbulence, Heat and Mass Transfer 11</strong> </em>conference in Tokyo.</p> <p><strong>Title:</strong> Refractive Index Determination of Dynamic Droplets in Flow by Analyzing Light Scattering Signals with a Machine Learning Approach <br><strong>Authors:</strong> W. Schaefer<br><strong>Affiliation:</strong> ai-quanton GmbH, Dr.-Werner-Freyberg-Str. 7, 69514 Laudenbach, Germany <br><strong>Contact:</strong> info@ai-quanton.com </p> <p>The following data files are provided:</p> <ul> <li><strong>Dataset_40_4ch1234.rar (unpacked: Dataset_40_4ch1234.pth)</strong></li> <li><strong>M1_SegmentsTHR40.csv</strong></li> <li><strong>SegmentsTHR40.rar (unpacked: M1_SegmentsTHR40.csv ... M55_SegmentsTHR40.csv)</strong></li> <li><strong>Model_weights_4ch1234.pth</strong></li> </ul> <p> </p> <p><strong>Dataset_40_4ch1234.pth</strong> is a file, containing a ready-to-use dataset of 4-channel signals prepared for use in Python scripts.</p> <p><strong>M1_SegmentsTHR40.csv </strong>is an example of a file used for storing and loading light scattering signals of individual droplets with corresponding additional data. The meaning of each column is:</p> <p>'MID' – measurement ID</p> <p>'FID' – frame ID</p> <p>'SID' – signal ID</p> <p>'CID' – channel ID</p> <p>'NOP' – number of parts</p> <p>'PNM' – part number</p> <p>'TCH' – trigger channel</p> <p>'TLE' – trigger level</p> <p>'TID' – trigger ID</p> <p>'CON' – label used for training</p> <p><strong>SegmentsTHR40.rar</strong> is an archived folder containing .csv files, the same format as M1_SegmentsTHR40.csv.</p> <p><strong>Model_weights_4ch1234.pth </strong>contains weights for a model trained on data from all 4 channels.</p> <p> </p> <p><strong>External files:</strong></p> <p>The correcponding repository to this dataset is published on Azure Dev Ops: <a href="https://dev.azure.com/ai-quanton/PBa202">https://dev.azure.com/ai-quanton/PBa202</a><br>This repository contains the Python script developed for a neural network that determines the refractive index of single droplets by analyzing light scattering signals generated as they pass through a Gaussian beam. </p> <p>The script is designed to build and test a machine learning model capable of accurately predicting refractive indices from light scattering data in dynamic spray environments.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.