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

Ponciano, Vasco; Pires, Ivan Miguel; Ribeiro, Fernando Reinaldo; Garcia, Nuno M.; Villasana, María Vanessa; Zdravevski, Eftim; Lameski, Petre

doi:10.3390/computers9030055

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

Journal

Computers

Date Issued

2020-06-29

Author(s)

Ponciano, Vasco

Pires, Ivan Miguel

Ribeiro, Fernando Reinaldo

Garcia, Nuno M.

Villasana, María Vanessa

DOI

10.3390/computers9030055

Abstract

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.

File(s)

Name

2020_06 computers-09-00055 - ECG-EEG exploration.pdf

Size

1.15 MB

Format

Adobe PDF

Checksum

(MD5):9d906bfac6a6de40c0e340fe47597bb9