Experimental Study for Determining the Parameters Required for Detecting ECG and EEG Related Diseases during the Timed-Up and Go Test
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, includin...
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doaj-f775cc0e34294c168a9b91247b46d77d2020-11-25T03:36:59ZengMDPI AGComputers2073-431X2020-08-019676710.3390/computers9030067Experimental Study for Determining the Parameters Required for Detecting ECG and EEG Related Diseases during the Timed-Up and Go TestVasco Ponciano0Ivan Miguel Pires1Fernando Reinaldo Ribeiro2María Vanessa Villasana3Maria Canavarro Teixeira4Eftim Zdravevski5R&D Unit in Digital Services, Applications and Content, Polytechnic Institute of Castelo Branco, 6000-767 Castelo Branco, PortugalInstituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, PortugalR&D Unit in Digital Services, Applications and Content, Polytechnic Institute of Castelo Branco, 6000-767 Castelo Branco, PortugalFaculty of Health Sciences, Universidade da Beira Interior, 6200-506 Covilhã, PortugalUTC de Recursos Naturais e Desenvolvimento Sustentável, Polytechnique Institute of Castelo Branco, 6001-909 Castelo Branco, PortugalFaculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, North MacedoniaThe 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.https://www.mdpi.com/2073-431X/9/3/67diseaseselectrocardiographyelectroencephalographytimed-up and go testsensorsmobile devices |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Vasco Ponciano Ivan Miguel Pires Fernando Reinaldo Ribeiro María Vanessa Villasana Maria Canavarro Teixeira Eftim Zdravevski |
spellingShingle |
Vasco Ponciano Ivan Miguel Pires Fernando Reinaldo Ribeiro María Vanessa Villasana Maria Canavarro Teixeira Eftim Zdravevski Experimental Study for Determining the Parameters Required for Detecting ECG and EEG Related Diseases during the Timed-Up and Go Test Computers diseases electrocardiography electroencephalography timed-up and go test sensors mobile devices |
author_facet |
Vasco Ponciano Ivan Miguel Pires Fernando Reinaldo Ribeiro María Vanessa Villasana Maria Canavarro Teixeira Eftim Zdravevski |
author_sort |
Vasco Ponciano |
title |
Experimental Study for Determining the Parameters Required for Detecting ECG and EEG Related Diseases during the Timed-Up and Go Test |
title_short |
Experimental Study for Determining the Parameters Required for Detecting ECG and EEG Related Diseases during the Timed-Up and Go Test |
title_full |
Experimental Study for Determining the Parameters Required for Detecting ECG and EEG Related Diseases during the Timed-Up and Go Test |
title_fullStr |
Experimental Study for Determining the Parameters Required for Detecting ECG and EEG Related Diseases during the Timed-Up and Go Test |
title_full_unstemmed |
Experimental Study for Determining the Parameters Required for Detecting ECG and EEG Related Diseases during the Timed-Up and Go Test |
title_sort |
experimental study for determining the parameters required for detecting ecg and eeg related diseases during the timed-up and go test |
publisher |
MDPI AG |
series |
Computers |
issn |
2073-431X |
publishDate |
2020-08-01 |
description |
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. |
topic |
diseases electrocardiography electroencephalography timed-up and go test sensors mobile devices |
url |
https://www.mdpi.com/2073-431X/9/3/67 |
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