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Open AccessArticle

Machine Learning Techniques with ECG and EEG Data: An Exploratory Study

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R&D Unit in Digital Services, Applications and Content, Polytechnic Institute of Castelo Branco, 6000-767 Castelo Branco, Portugal
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Altranportugal, 1990-096 Lisbon, Portugal
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Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
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Department of Computer Science, Polytechnic Institute of Viseu, 3504-510 Viseu, Portugal
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Faculty of Health Sciences, Universidade da Beira Interior, 6200-506 Covilhã, Portugal
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Faculty of Computer Science and Engineering, University Ss Cyril and Methodius, 1000 Skopje, North Macedonia
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Author to whom correspondence should be addressed.
Computers 2020, 9(3), 55; https://doi.org/10.3390/computers9030055
Received: 1 June 2020 / Revised: 25 June 2020 / Accepted: 28 June 2020 / Published: 29 June 2020
(This article belongs to the Special Issue Machine Learning for EEG Signal Processing)
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. View Full-Text
Keywords: artificial intelligence; electrocardiography; electroencephalography; feature extraction; recognition of diseases artificial intelligence; electrocardiography; electroencephalography; feature extraction; recognition of diseases
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Ponciano, V.; Pires, I.M.; Ribeiro, F.R.; Garcia, N.M.; Villasana, M.V.; Zdravevski, E.; Lameski, P. Machine Learning Techniques with ECG and EEG Data: An Exploratory Study. Computers 2020, 9, 55.

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