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Exasens Data Set
Download: Data Folder, Data Set Description

Abstract: This repository introduces a novel dataset for the classification of 4 groups of respiratory diseases: Chronic Obstructive Pulmonary Disease (COPD), asthma, infected, and Healthy Controls (HC).

Data Set Characteristics:  

Multivariate

Number of Instances:

399

Area:

Life

Attribute Characteristics:

Integer

Number of Attributes:

4

Date Donated

2020-04-22

Associated Tasks:

Classification, Clustering

Missing Values?

Yes

Number of Web Hits:

3628


Source:

Pouya Soltani Zarrin, soltani '@' ihp-microelectronics.com, IHP–Leibniz-institute for innovative microelectronics, 15236 Frankfurt (Oder), Germany
Niels Roeckendorf, Research Center Borstel–Leibniz Lung Center, Priority Area Asthma & Allergy, Division of Mucosal Immunology & Diagnostics, Member of Leibniz Health Technologies and German Center for Lung Research, 23845 Borstel, Germany


Data Set Information:

The Exasens dataset includes demographic information on 4 groups of saliva samples (COPD-Asthma-Infected-HC) collected in the frame of a joint research project, Exasens ([Web Link]), 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 ([Web Link]).

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)


Relevant Papers:

P. S. Zarrin, N. Roeckendorf, and C. Wenger. In-vitro Classification of Saliva Samples of COPD Patients and Healthy Controls Using Non-perceptron Machine Learning Tools. Annals of biomedical engineering, 2020.

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.

Krause, T., Ramaker, K., Röckendorf, N., Sinnecker, H. and Frey, A., 2016. Airway mucins–suitable biomarkers to predict an upcoming exacerbation in COPD and asthma?. Pneumologie, 70(07), p.P43.



Citation Request:

The authors acknowledge the Federal Ministry for Education and Research (BMBF) of Germany for funding this work. The authors thank the BioMaterialBank Nord (BMB Nord), popgen 2.0 network (P2N), and the German Center for Lung Research for the collection of saliva samples and the staff at IHP and FZ Borstel-Leibniz Lung Center for their precious support with this work. In case of using the introduced Exasens dataset please cite the following papers:

P. S. Zarrin, N. Roeckendorf, and C. Wenger. In-vitro Classification of Saliva Samples of COPD Patients and Healthy Controls Using Non-perceptron Machine Learning Tools. Annals of biomedical engineering, 2020.


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