Exasens: a novel dataset for the classification of saliva samples of COPD patients

Citation Author(s):
Soltani Zarrin
IHP–Leibniz-Institut für innovative Mikroelektronik
Research Center Borstel–Leibniz Lung Center
IHP–Leibniz-Institut für innovative Mikroelektronik
Submitted by:
Pouya Soltani Zarrin
Last updated:
Tue, 05/17/2022 - 22:21
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This repository introduces a novel dataset for the classification of Chronic Obstructive Pulmonary Disease (COPD) patients and Healthy Controls. The Exasens dataset includes demographic information on 4 groups of saliva samples (COPD-HC-Asthma-Infected) collected in the frame of a joint research project, Exasens (https://www.leibniz-healthtech.de/en/research/projects/bmbf-project-exasens/), at the Research Center Borstel, BioMaterialBank Nord (Borstel, Germany). The sampling procedure of the patient materials was approved by the local ethics committee of the University of Luebeck under the approval number AZ-16-167 and a written informed consent was obtained from all subjects. A permittivity biosensor, developed at IHP Microelectronics (Frankfurt Oder, Germany), was used for the dielectric characterization of the saliva samples for classification purposes (https://doi.org/10.3390/healthcare7010011).



Definition of 4 sample groups included within the Exasens dataset:

(I) Outpatients and hospitalized patients with COPD without acute respiratory infection (COPD).

(II) Outpatients and hospitalized patients with asthma without acute respiratory infections (Asthma).

(III) Patients with respiratory infections, but without COPD or asthma (Infected).

(IV) Healthy controls without COPD, asthma, or any respiratory infection (HC).

Attribute Information:

1- Diagnosis (COPD-HC-Asthma-Infected)

2- ID

3- Age

4- Gender (1=male, 0=female)

5- Smoking Status (1=Non-smoker, 2=Ex-smoker, 3=Active-smoker)

6- Saliva Permittivity:

a) Imaginary part (Min(Δ)=Absolute minimum value, Avg.(Δ)=Average)  

b) Real part (Min(Δ)=Absolute minimum value, Avg.(Δ)=Average) 

In case of using the introduced Exasens dataset or the proposed classification methods, please cite either of following references:

  • P. S. Zarrin, N. Roeckendorf and C. Wenger., "In-vitro Classification of Saliva Samples of COPD Patients and Healthy Controls Using Machine Learning Tools," in IEEE Access, doi: 10.1109/ACCESS.2020.3023971.

  • P.S. Zarrin, Zahari, F., Mahadevaiah, M.K. et al. Neuromorphic on-chip recognition of saliva samples of COPD and healthy controls using memristive devices. Sci Rep 10, 19742 (2020). https://doi.org/10.1038/s41598-020-76823-7

  • Soltani Zarrin, P.; Ibne Jamal, F.; Roeckendorf, N.; Wenger, C. Development of a Portable Dielectric Biosensor for Rapid Detection of Viscosity Variations and Its In Vitro Evaluations Using Saliva Samples of COPD Patients and Healthy Control. Healthcare 2019, 7, 11.

  • Soltani Zarrin, P.; Jamal, F.I.; Guha, S.; Wessel, J.; Kissinger, D.; Wenger, C. Design and Fabrication of a BiCMOS Dielectric Sensor for Viscosity Measurements: A Possible Solution for Early Detection of COPD. Biosensors 2018, 8, 78.

  • P.S. Zarrin and C. Wenger. Pattern Recognition for COPD Diagnostics Using an Artificial Neural Network and Its Potential Integration on Hardware-based Neuromorphic Platforms. Springer Lecture Notes in Computer Science (LNCS), Vol. 11731, pp. 284-288, 2019.

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