THE USE OF NEURAL NETWORKS FOR THE AUTOMATIC ANALYSIS OF ELECTROCARDIOGRAMS IN DIAGNOSIS OF CARDIOVASCULAR DISEASES

Abstract

ObjectivesThe aim of the work is to use neural networks to detect characteristic ECG signals that determine heart rhythm abnormalities and detect the corresponding heart disease. One of the most important factors for the timely provision of medical care is the rapid and accurate obtaining of information about the patient's state of health. The timeliness of diagnosis is often the main factor determining the patient's prognosis. While the accuracy of cardiological diagnosis has significantly increased in recent years due to the wide application of both instrumental and laboratory research methods, the percentage of diagnostic errors in cardiology remains high. Electrocardiography (ECG) consists ina non-invasive process of interpreting the electrical activity of the heart, allowing the speed and regularity of the heartbeat to be assessed. These data are then used to determine any heart defects or pathologies. However, automatic ECG analysis remains a challenging theoretical and practical task.MethodsA MATLAB 8.6 (R2015b) Neural Network Toolbox was used to simulate artificial neural networksduring the design. A backpropagation algorithm was used for trainingthe neural network. The efficiency of the developed neural network model for ECG analysis was investigated using the MIT-BIH arrhythmia database.ResultsThe accuracy of detection and extraction of the components of the ECG signal shows that the developed neural network model can be successfully used to detect heart diseases among patients. The sensitivity of the model was 71%, with a specificity of 89%. The elaboration of existing and development of new methods for processing electrocardiographic data allow the problem of timely diagnosis and prevention of cardiovascular diseases to be solvedat early stages of their detection.ConclusionThe accuracy of detection and extraction of the ECG signal components shows that the developed neural network model can be used to detect heart diseases among patients.