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- Machine learning techniques with ECG and EEG data: an exploratory studyPublication . Ponciano, Vasco Rafael Gaspar; Pires, Ivan M.; Ribeiro, Fernando Reinaldo; Garcia, Nuno M.; Villasana, María Vanessa; Lameski, Petre; Zdravevski, EftimElectrocardiography (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.
- Detection of diseases based on electrocardiography and electroencephalography signals embedded in different devices: an exploratory studyPublication . Ponciano, Vasco Rafael Gaspar; Pires, Ivan M.; Ribeiro, Fernando Reinaldo; Villasana, María Vanessa; Garcia, Nuno M.; Leithardt, ValderiNowadays, cardiac and brain disorders are dispersed over the world, where an early detection allows the treatment and prevention of other related healthcare problems. Technologically, this detection is difficult to perform, and the use of technology and artificial intelligence techniques may automate the accurate detection of different diseases. This paper presents the research on the different techniques and parameters for the detection of diseases related to Electrocardiography (ECG) and Electroencephalography (EEG) signals. Previously experiments related to the performance of the Timed-Up and Go test with elderly people acquired different signals from people with different diseases. This study identifies different parameters and methods that may be used for the identification of different diseases based on the acquired data.