Machine learning techniques with ECG and EEG data: an exploratory study

article

Ponciano, Vasco
Pires, Ivan
Ribeiro, Fernando Reinaldo
Garcia, Nuno
Villasana, Maria
Lameski, Petre
Zdravevski, Eftim

MDPI

PONCIANO, Vasco [et al.] (2020) - Machine learning techniques with ECG and EEG data: an exploratory study. Computers. ISSN 2073-431X . Vol. 9, nº. 3, p. 55. DOI: https://doi.org/10.3390/computers9030055

2073-431X

Machine learning techniques with ECG and EEG data: an exploratory study

Artificial intelligence
Electrocardiography
Electroencephalography
Feature extraction
Recognition of diseases

UIDB/EEA/50008/2020.

2020-07-07T08:59:24Z
2020-07-07T08:59:24Z
2020-06

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.
info:eu-repo/semantics/publishedVersion

openAccess
http://creativecommons.org/licenses/by/4.0/

eng