Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Experimental Study for Determining the Parameters Required for Detecting ECG and EEG Related Diseases During the Timed-Up and Go Test

Version 1 : Received: 5 August 2020 / Approved: 6 August 2020 / Online: 6 August 2020 (10:46:57 CEST)

A peer-reviewed article of this Preprint also exists.

Ponciano, V.; Pires, I.M.; Ribeiro, F.R.; Villasana, M.V.; Teixeira, M.C.; Zdravevski, E. Experimental Study for Determining the Parameters Required for Detecting ECG and EEG Related Diseases During the Timed-Up and Go Test. Computers 2020, 9, 67. Ponciano, V.; Pires, I.M.; Ribeiro, F.R.; Villasana, M.V.; Teixeira, M.C.; Zdravevski, E. Experimental Study for Determining the Parameters Required for Detecting ECG and EEG Related Diseases During the Timed-Up and Go Test. Computers 2020, 9, 67.

Journal reference: Computers 2020, 9, 67
DOI: 10.3390/computers9030067

Abstract

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, 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.

Subject Areas

Diseases; electrocardiography; electroencephalography; Timed-Up and Go test; sensors; mobile devices; feature detection; diseases; older adults

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