Machine Learning Techniques with ECG and EEG Data: An Exploratory Study
Electrocardiography (ECG) and electroencephalography (EEG) are powerful tools in medicine for the analysis of various diseases. The emergence of affordable ECG and EEG sensors and ubiquitous mobile devices provides an opportunity to make such analysis accessible to everyone. In this paper, we propos...
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doaj-920cd883c2b049b0b771a8a094fe45b82020-11-25T03:23:42ZengMDPI AGComputers2073-431X2020-06-019555510.3390/computers9030055Machine Learning Techniques with ECG and EEG Data: An Exploratory StudyVasco Ponciano0Ivan Miguel Pires1Fernando Reinaldo Ribeiro2Nuno M. Garcia3María Vanessa Villasana4Petre Lameski5Eftim Zdravevski6R&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, PortugalInstituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, PortugalFaculty of Health Sciences, Universidade da Beira Interior, 6200-506 Covilhã, PortugalFaculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, North MacedoniaFaculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, North MacedoniaElectrocardiography (ECG) and electroencephalography (EEG) are powerful tools in medicine for the analysis of various diseases. The emergence of affordable ECG and EEG sensors and ubiquitous mobile devices provides an opportunity to make such analysis accessible to everyone. In this paper, we propose the implementation of a neural network-based method for the automatic identification of the relationship between the previously known conditions of older adults and the different features calculated from the various signals. The data were collected using a smartphone and low-cost ECG and EEG sensors during the performance of the timed-up and go test. Different patterns related to the features extracted, such as heart rate, heart rate variability, average QRS amplitude, average R-R interval, and average R-S interval from ECG data, and the frequency and variability from the EEG data were identified. A combination of these parameters allowed us to identify the presence of certain diseases accurately. The analysis revealed that the different institutions and ages were mainly identified. Still, the various diseases and groups of diseases were difficult to recognize, because the frequency of the different diseases was rare in the considered population. Therefore, the test should be performed with more people to achieve better results.https://www.mdpi.com/2073-431X/9/3/55Artificial intelligenceelectrocardiographyelectroencephalographyfeature extractionrecognition of diseases |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Vasco Ponciano Ivan Miguel Pires Fernando Reinaldo Ribeiro Nuno M. Garcia María Vanessa Villasana Petre Lameski Eftim Zdravevski |
spellingShingle |
Vasco Ponciano Ivan Miguel Pires Fernando Reinaldo Ribeiro Nuno M. Garcia María Vanessa Villasana Petre Lameski Eftim Zdravevski Machine Learning Techniques with ECG and EEG Data: An Exploratory Study Computers Artificial intelligence electrocardiography electroencephalography feature extraction recognition of diseases |
author_facet |
Vasco Ponciano Ivan Miguel Pires Fernando Reinaldo Ribeiro Nuno M. Garcia María Vanessa Villasana Petre Lameski Eftim Zdravevski |
author_sort |
Vasco Ponciano |
title |
Machine Learning Techniques with ECG and EEG Data: An Exploratory Study |
title_short |
Machine Learning Techniques with ECG and EEG Data: An Exploratory Study |
title_full |
Machine Learning Techniques with ECG and EEG Data: An Exploratory Study |
title_fullStr |
Machine Learning Techniques with ECG and EEG Data: An Exploratory Study |
title_full_unstemmed |
Machine Learning Techniques with ECG and EEG Data: An Exploratory Study |
title_sort |
machine learning techniques with ecg and eeg data: an exploratory study |
publisher |
MDPI AG |
series |
Computers |
issn |
2073-431X |
publishDate |
2020-06-01 |
description |
Electrocardiography (ECG) and electroencephalography (EEG) are powerful tools in medicine for the analysis of various diseases. The emergence of affordable ECG and EEG sensors and ubiquitous mobile devices provides an opportunity to make such analysis accessible to everyone. In this paper, we propose the implementation of a neural network-based method for the automatic identification of the relationship between the previously known conditions of older adults and the different features calculated from the various signals. The data were collected using a smartphone and low-cost ECG and EEG sensors during the performance of the timed-up and go test. Different patterns related to the features extracted, such as heart rate, heart rate variability, average QRS amplitude, average R-R interval, and average R-S interval from ECG data, and the frequency and variability from the EEG data were identified. A combination of these parameters allowed us to identify the presence of certain diseases accurately. The analysis revealed that the different institutions and ages were mainly identified. Still, the various diseases and groups of diseases were difficult to recognize, because the frequency of the different diseases was rare in the considered population. Therefore, the test should be performed with more people to achieve better results. |
topic |
Artificial intelligence electrocardiography electroencephalography feature extraction recognition of diseases |
url |
https://www.mdpi.com/2073-431X/9/3/55 |
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