Artefact-free and distorted capnogram segments

Citation Author(s):
Ismail
M. El-Badawy
Submitted by:
Ismail M. EL-BADAWY
Last updated:
Wed, 03/20/2024 - 00:06
DOI:
10.21227/jcf5-9872
Data Format:
and text (.txt)
Research Article Link:
Effective Machine Learning for Classifying Artefact-Free and Distorted Capnogram Segments Using Simple Time-Domain Features
License:
Creative Commons Attribution
448 Views
Categories:
Signal Processing
Digital signal processing
Machine Learning
Biomedical and Health Sciences
Biophysiological Signals
Keywords:
capnogram; COPD; Asthma; Pulmonary diseases; artefacts
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Abstract 

This dataset has been employed in the following articles:

https://ieeexplore.ieee.org/document/9682692

https://ieeexplore.ieee.org/document/9871051

https://content.iospress.com/articles/technology-and-health-care/thc202198

The dataset comprises 100 clean and 100 distorted segments acquired from recorded capnogram signals. The time duration of each segment is 15 seconds, and hence each .txt file contains 300 CO2 sample value becuase the sampling rate of the capnography device is 20 samples/sec.  

Instructions: 

Each text file is given a name according to whether it contains a regular (artefact-free) or irregular (distorted) capnogram segment. For example the ninth distorted segment is given the name: Sample_9_irregular.txt 

 

Each text file contains 300 CO2 sample values, and the time duration between each  two consecutive samples is 0.05 s because the sampling rate of the employed capnography device is 20 samples/sec.

Comments

This dataset has been employed in the following articles:

https://ieeexplore.ieee.org/document/9682692

https://content.iospress.com/articles/technology-and-health-care/thc202198

Submitted by Ismail M. EL-BADAWY on Sat, 01/15/2022 - 03:38

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Documentation

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File Readme.txt819 bytes

 

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