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

eng Article in English DOI: 10.14313/PAR_252/21

Sandra Śmigiel *, Tomasz Topoliński *, Damian Ledziński **, send Tomasz Andrysiak ** * Bydgoszcz University of Science and Technology, Faculty of Mechanical Engineering ** Bydgoszcz University of Science and Technology, Faculty of Telecommunications, Computer Science and Electrical Engineering

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Abstract

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.

Keywords

classification, ECG signal, IoT, LoRa, machine learning, mobile device, neural network, PTB-XL database

System do monitorowania sygnału EKG z wykorzystaniem metod uczenia maszynowego i technologii LoRa

Streszczenie

Elektrokardiogram (EKG) jest pierwszym krokiem w diagnozowaniu chorób serca. Zaburzenia rytmu serca są jednymi z wczesnych objawów chorób serca, które mogą przyczynić się do zawału serca, udaru mózgu lub nagłej śmierci pacjenta. Znaczenie EKG wzrosło wraz z rozwojem technologii opartych na uczeniu maszynowym i zdalnym monitorowaniu parametrów życiowych. W szczególności wczesne wykrywanie arytmii ma ogromne znaczenie, jeśli chodzi o diagnozowanie pacjenta z chorobą serca. Jest to możliwe dzięki rozpoznawaniu i klasyfikowaniu patologicznych wzorców w sygnale EKG. W artykule przedstawiono system zdalnego monitorowania sygnałów EKG wzbogacony o badania eksperymentalne nad zastosowaniem modeli uczenia maszynowego (ML) z grupy opartych na drzewach i architekturze sieci neuronowych, w kontekście klasyfikacji chorób serca. Badania przeprowadzono z wykorzystaniem publicznie dostępnej bazy danych sygnałów EKG, tj. PTB-XL. Wyniki analizowano w kontekście dokładności klasyfikacji dla 2, 5 i 15 klas chorób serca. Nowością w pracy jest wskazanie modeli uczenia maszynowego i architektury sieci neuronowych, jakie można zastosować w urządzeniach IoT. W oparciu o przeprowadzone badania udowodniono, że proponowane rozwiązanie może działać w czasie rzeczywistym na urządzeniach IoT.

Słowa kluczowe

baza danych PTB-XL, IoT, klasyfikacja, LoRa, sieci neuronowe, sygnał EKG, uczenie maszynowe, urządzenia mobilne

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