Recurrence Plot based Person Identification with ECG using CNN model

초록

With the COVID-19 pandemic and an aging population, there has been a rise in demand for homecare for patients with chronic diseases that require continuous monitoring outside of the hospital. One important bio-signal for such monitoring is an electrocardiogram (ECG), which measures the electrical activity of the heart and can detect dangerous conditions such as arrhythmias and myocardial infarctions. The application of deep learning classification algorithms to arrhythmia and myocardial infarction diagnosis has gained interest. However, to be effectively utilized in everyday life, a method to determine who performed the measurement is necessary. In this study, we evaluated the use of recurrence plot pre-processing and convolutional neural network (CNN) models to identify individuals based on their ECG signals. Our proposed method demonstrated high accuracy results across various CNN models and was capable of identifying individuals. © 2023 IEEE.

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