The data set contains 58 time series acquired from 16 chemical sensors under a gas flow modulation. The sensors were exposed to gaseous binary mixtures of acetone and ethanol at different concentrations.
The data set is published the UCI Machine Learning repository, named as Gas sensor array under flow modulation Data Set.
This repository at github.com is intended to collect code examples (R, python, Matlab, etc) for reproducible analysis applied to the published data.
Figure: PCA trajectories of the sensors' signals pre-processed by a high-pass filter (the first 4 respiration cycles). Trajectories for three gas classes are presented: acetone at 0.05 vol.% (orange), ethanol at 0.01 vol.% (violet) and their binary mixture (blue). See more details on http://neurochem.sisbio.recerca.upc.edu/?p=311.
- R code: examples/R
- Ziyatdinov2014: The first data analysis of the data set was presented in (Ziyatdinov et al., 2014), and the results reported there should be considered as a reference. The study aimed to characterize and explore the sensor signals in response to the modulated gas flow at a fixed respiration frequency. It was expected to confirm a superior performance of the proposed system under the gas flow modulation on the early detection scenario.
To be able to run the code, one needs to download the data files rawdata.csv.gz and features.csv from the UCI Machine Learning Repository website and save them to a local folder named dat/.