Analysis on Electrocardiogram Measurement Performance Using Metal Electrode Based on 1-D Convolutional Neural Network for Application of Wearable Device

Abstract

Disposable electrode is mainly used when measuring ECG signals. However, disposable electrode has low re-usability and durability, making it difficult to obtain high-quality signals when used for a long term. In this paper, we evaluated the applicability of metal plates and conductive tapes as new reference electrode to replace disposable electrode. For evaluation, we implemented a 1D CNN-based training model and an ECG measurement system to collect data. The implemented neural network was trained based on the MIT-BIH arrhythmia data set, and 5 classes of ECG classification including normal signals are possible. For measurement evaluation of metal plates, conductive tapes, and disposable electrode, field tests were conducted using the trained model. The ECG class classification of the implemented CNN training model confirmed a classification accuracy of more than 96%, and compared with previously studied pattern matching algorithms and other studies, confirmed excellent detection performance. In the field test, performance evaluation of arrhythmia classification was performed using a neural network trained on ECG signals obtained with disposable electrode, metal plates, and conductive tape. The ECG signals for evaluation used in the experiment, 300 samples were measured for each class from 10 subjects. As a result of the experiment, it was confirmed that the disposable electrode was classified as 98.59%, the metal plate was classified as 95.26%, and the conductive tape was classified as 96.13%. It is inconvenient to measure ECG using disposable electrode in everyday life. However, there is no difference in ECG detection performance in the case of a metal plate and a conductive tape compared to a disposable electrode. In addition, metal plates and conductive tapes can be easily applied to existing wearables because they can be reused and long term.