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README.md
The UniCA ElectroTastegram Database (PROP) contains 39 differential biopotential measurements recorded from the tongues of as many healthy voluntary human subjects (16 males, 23 females, equally divided into the three PROP taster status classes), during a stimulation with 30uL, 3.2 mmol/L solution of 6-n-propylthiouracil (PROP).
Background
In the last decades, PROP tasting has gained considerable attention as a paradigmatic stimulus of general taste perception and oral marker for food preferences and eating behavior that ultimately impacts nutritional status and health. about 25%-30% of the Caucasian population does not perceive PROP taste (non-tasters), whereas the remaining does (tasters). Among the tasters, supertasters perceive PROP bitterness at levels far above the average (according to qualitative tests), whereas as their name suggests, medium tasters (MTs) occupy the middle ground between supertasters (STs) and non-tasters (NTs).
In [1], a novel non-invasive technique for the direct measurement of the degree of activation of peripheral taste function in humans through electrophysiological recordings was presented and used to study the relationship between amplitude and rate of the biopotential evoked by the PROP stimulus and subjects' PROP genotype and phenotype. based on the evidence of such a seminal work, [2] addressed for the first time the problem of evaluating the effectiveness and limitations of supervised classifiers in the automatic identification of PROP taster categories: NT, ST, MT. This dataset presents all the biopotentials used in that work.
Data Collection
This data set consists of 39 differential biopotential measurements recorded from the tongues of as many healthy voluntary human subjects (16 males, 23 females, equally divided into the three PROP taster status classes), during a stimulation with 30uL, 3.2 mmol/L solution of 6-n-propylthiouracil (PROP).
The depolarization measurement after the application of PROP stimulus to the tongue was performed by using a custom comformable electrode in contact with the dorsal surface of the tongue, and a second electrode, consisting simply of a silver wire rolled into a ball, placed on the ventral side of the tongue as a second terminal for the differential measurement. A ground disposable gelled electrode was applied on the cheek. The recording device was a 32-channel Porti7 portable physiological measurement system (TMSI, The Netherlands), the electrodes were connected to the signal terminals of the AUX channels, featuring a dynamic range (±3 V) that is broader than the standard bipolar ExG channels (150 mV) in order to prevent saturation of the analog amplifier. The positive and negative inputs of the AUX channel were connected to the electrode on the dorsal and ventral sides of the tongue, respectively.
The analog signals were sampled at fs = 2048 Hz and digitized at 22 bits (1.43 uV resolution). At this sampling frequency, the actual bandwidth was limited by a digital decimation filter with a cut-off of approximately 550 Hz. The signals in this dataset present the same duration (26 s), and are recorded from about 6 seconds before the application of the stimulus to about 20 seconds from it. Due to the smooth trend of the depolarization, all signals were filtered with a low-pass equiripple finite impulse response (FIR) filter, with a cut-off frequency of 6 Hz (86th order). The samples values reflect an amplitude in mV. The file "info.txt" reports the PROP taster categories (NT, ST, MT) for the signals in the dataset (ordered). Each header file reports the subject's gender and PROP taster category.
Files
Files have been converted to WFDB format by the mat2wfdb Matlab function, with automatic parameters setup, and can be easily read by using the rdsamp function.
Contributors
Danilo Pani, Ilenia Usai, Piero Cosseddu, Luigi Raffo, Annalisa Bonfiglio (Department of Electrical and Electronic Engineering, University of Cagliari, Italy) and Melania Melis, Giorgia Sollai, Roberto Crnjar, Iole Tomassini Barbarossa (Department of Biomedical Sciences, University of Cagliari, Italy).
When using this resource, please cite the original publication:
Please include the standard citation for PhysioNet: (show more options)
Goldberger, A., Amaral, L., Glass, L., Hausdorff,
J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000).
PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research
resource for complex physiologic signals. Circulation [Online]. 101
(23), pp. e215–e220.
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