Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/14072
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dc.contributor.authorPonciano, Vascoen_US
dc.contributor.authorPires, Ivan Miguelen_US
dc.contributor.authorRibeiro, Fernando Reinaldoen_US
dc.contributor.authorVillasana, María Vanessaen_US
dc.contributor.authorCanavarro Teixeira, Mariaen_US
dc.contributor.authorZdravevski, Eftimen_US
dc.date.accessioned2021-07-06T14:06:52Z-
dc.date.available2021-07-06T14:06:52Z-
dc.date.issued2020-08-27-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/14072-
dc.description.abstract<jats:p>The use of smartphones, coupled with different sensors, makes it an attractive solution for measuring different physical and physiological features, allowing for the monitoring of various parameters and even identifying some diseases. The BITalino device allows the use of different sensors, including Electroencephalography (EEG) and Electrocardiography (ECG) sensors, to study different health parameters. With these devices, the acquisition of signals is straightforward, and it is possible to connect them using a Bluetooth connection. With the acquired data, it is possible to measure parameters such as calculating the QRS complex and its variation with ECG data to control the individual’s heartbeat. Similarly, by using the EEG sensor, one could analyze the individual’s brain activity and frequency. The purpose of this paper is to present a method for recognition of the diseases related to ECG and EEG data, with sensors available in off-the-shelf mobile devices and sensors connected to a BITalino device. The data were collected during the elderly’s experiences, performing the Timed-Up and Go test, and the different diseases found in the sample in the study. The data were analyzed, and the following features were extracted from the ECG, including heart rate, linear heart rate variability, the average QRS interval, the average R-R interval, and the average R-S interval, and the EEG, including frequency and variability. Finally, the diseases are correlated with different parameters, proving that there are relations between the individuals and the different health conditions.</jats:p>en_US
dc.language.isoenen_US
dc.publisherMDPI AGen_US
dc.relation.ispartofComputersen_US
dc.titleExperimental Study for Determining the Parameters Required for Detecting ECG and EEG Related Diseases during the Timed-Up and Go Testen_US
dc.typeJournal Articleen_US
dc.identifier.doi10.3390/computers9030067-
dc.identifier.urlhttps://www.mdpi.com/2073-431X/9/3/67/pdf-
dc.identifier.volume9-
dc.identifier.issue3-
item.fulltextWith Fulltext-
item.grantfulltextopen-
crisitem.author.deptFaculty of Computer Science and Engineering-
Appears in Collections:Faculty of Computer Science and Engineering: Journal Articles
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