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

Abstract

<jats:p>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.</jats:p>

The First Steps in Artificial Intelligence Development in Medicine in Uzbekistan

“…The digital data of the ECG waveform output from Bitalino was input to the AI system [25]. We used a neural network called convolutional neural network (CNN) [26], which is one of the machine learning methods used for image recognition.…”

Extraction of notable points from ECG data: A description of a dataset related to 30-s seated and 30-s stand up

“…The presented work is focused on data provided for related projects with Electrocardiography data for pattern identification [15] , [16] , [17] (with signal processing or algorithms) that allows the identification of abnormal patterns or other relevant information within the signal for 30 s sitting and 30 s standing test. Thus, with the simple movement of an individual, it will be possible to verify how medicine can be directed to the individual with patient-based methods.…”

Industry 4.0 and Digitalisation in Healthcare

“…The use of machine learning enables the processing of the extracted data, such as heart rate, pulse, intervals, variability from ECG/EEG data, etc. A combination of these parameters can help to identify the existence of heart-related diseases [ 166 ].…”