The ECG Signal Monitoring System Using Machine Learning Methods and LoRa Technology

An electrocardiogram (ECG) is the first step in diagnosing heart disease. Heart rhythm abnormalities are among the early signs of heart disease, which can contribute to a patient’s heart attack, stroke, or sudden death. The importance of the ECGs has increased with the development of technologies based on machine learning and remote monitoring of vital signs. In particular, early detection of arrhythmias is of great importance when it comes to diagnosing a patient with heart disease. This is made possible through recognizing and classifying pathological patterns in the ECG signal. This paper presents a system for mobile monitoring of ECG signals enriched with the results of the study of the application of machine learning models from the group of Tree-based ML techniques and Neural Networks in the context of heart disease classification. The research was carried out through the use of the publicly available PTB-XL database of the ECG signals. The results were analyzed in the context of classification accuracy for 2, 5 and 15 classes of heart disease. Moreover, a novelty in the work is the proposal of machine learning techniques and architectures neural networks, which, have been selected to be applicable to IoT devices. It has been proven that the proposed solution can run in real time on IoT devices.