Title: COVID-19: Early detection using K-Means and DBSCAN algorithms on real-time patient data

Type Dataset Ali, Omer, Ishak, Mohamad Khairi, Wuttisittikulkij, Lunchakorn, Bhatti, Muhammad Kamran Liaquat (2020): COVID-19: Early detection using K-Means and DBSCAN algorithms on real-time patient data. Zenodo. Dataset. https://zenodo.org/record/4115562

Authors: Ali, Omer (Universiti Sains Malaysia) ; Ishak, Mohamad Khairi (Universiti Sains Malaysia) ; Wuttisittikulkij, Lunchakorn (Department of Electrical Engineering Smart Wireless Communication Ecosystem Research Group Chulalongkorn University, Bangkok, Thailand) ; Bhatti, Muhammad Kamran Liaquat (Department of Electrical Engineering, NFC Institute of Engineering and Technology (NFC IET), Multan, Pakistan) ;

Links

• Item record in Zenodo
• Digital object URL

Summary

This release features the dataset recorded using our prototype hardware device (contact-based) that collects accelerometer and temperature readings to investigate breathing patterns, personal activity and cough patterns. As fever and cough are considered as two of the most common symptoms for COVID-19, our aim was to focus on these physiological features. In addition, research also states that all COVID-19 contractions showed elevated breathing patterns in patients, that could be easily identifiable from Eupnea state. Therefore, in this research project, we aimed at: 1. Designing a prototype chest-worn device to measure dynamic chest movements (to record breathing and cough patterns) 2. Detect different activity patterns 3. Record temperature variations during idle and active stage 4. Using unsupervised machine learning algorithm to detect anomalies by creating a composite score for (breathing and cough patterns as well as temperature). 

This release also features some code examples that were used for data pre-processing, feature identification and anomaly detection using (K-means and DBSCAN algorithms). 

It is important to note that this dataset should be considered and further investigated for preliminary exploratory analysis. The data was collected from healthy adults (that did not undergo COVID-19 clinical screening tests). Therefore, it must be clearly identified that this dataset DOES NOT represent positive COVID-19 contractions. 

More information

• DOI: 10.5281/zenodo.4115562
• Language: en

Subjects

• covid-19, dataset, anomaly detection, feature extraction

Dates

• Publication date: 2020
• Issued: October 21, 2020

Notes

Rights

• info:eu-repo/semantics/closedAccess Closed Access

Format

electronic resource

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