Abstract
Electrocardiogram (ECG) signal has been widely studied for biometric recognition (or subject identification) applications over the past decade. Many techniques have been offered as well, from traditional computing to machine learning techniques. However, most studies evaluate their proposed models by independently separating the datasets between healthy (normal) and unhealthy (abnormal/arrhythmia) heart conditions. Some studies even used the ECG signal datasets from subjects only under healthy heart conditions. We believe that developing an ECG-based biometric system that includes the heart, both under healthy and unhealthy conditions, is highly needed since the users might be in both conditions. To the best of our knowledge, this is the first study in the literature, which considers the combination of the dataset of ECG signals from subjects under the healthy heart and unhealthy (arrhythmia) conditions in training, validation, and even testing processes. We combined the datasets from MIT-BIH Normal Sinus Rhythm, MIT-BIH Arrhythmia, and St Petersburg INCART 12-lead Arrhythmia databases obtained from Physionet. From the combination of those databases, we obtained a total of 32,628 datasets from 89 subjects which were later modeled using a machine learning technique, i.e., one-dimensional convolutional neural network (CONV1D). This study aims to build a light-weight yet reliable ECG-based biometric system by implementing only two layers of the convolutional neural network into those joined datasets. We evaluated the model's performance with k-fold cross-validation (k = 5) and yielded the accuracy and F1-scores of 99.8% And 99.8% respectively. We also proved that our model is a good fitting (not under/over-fitting) by providing the visualization plot of accuracy and loss. These findings show that our model is robust enough for ECG-based subject identification, which can be implemented for healthy and unhealthy heart conditions.
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Yuniarti, A.R., Rizal, S. (2022). Implementation of One-Dimensional Convolutional Neural Network for Individual Identification Based on ECG Signal. In: Triwiyanto, T., Rizal, A., Caesarendra, W. (eds) Proceedings of the 2nd International Conference on Electronics, Biomedical Engineering, and Health Informatics. Lecture Notes in Electrical Engineering, vol 898. Springer, Singapore. https://doi.org/10.1007/978-981-19-1804-9_26
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DOI: https://doi.org/10.1007/978-981-19-1804-9_26
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