Convolutional Neural Network for Individual Identification Using Phase Space Reconstruction of Electrocardiogram

Hsiao Lung Chan*, Hung Wei Chang, Wen Yen Hsu, Po Jung Huang, Shih Chin Fang

*此作品的通信作者

研究成果: 期刊稿件文章同行評審

3 引文 斯高帕斯(Scopus)

摘要

Electrocardiogram (ECG) biometric provides an authentication to identify an individual on the basis of specific cardiac potential measured from a living body. Convolutional neural networks (CNN) outperform traditional ECG biometrics because convolutions can produce discernible features from ECG through machine learning. Phase space reconstruction (PSR), using a time delay technique, is one of the transformations from ECG to a feature map, without the need of exact R-peak alignment. However, the effects of time delay and grid partition on identification performance have not been investigated. In this study, we developed a PSR-based CNN for ECG biometric authentication and examined the aforementioned effects. Based on a population of 115 subjects selected from the PTB Diagnostic ECG Database, a higher identification accuracy was achieved when the time delay was set from 20 to 28 ms, since it produced a well phase-space expansion of P, QRS, and T waves. A higher accuracy was also achieved when a high-density grid partition was used, since it produced a fine-detail phase-space trajectory. The use of a scaled-down network for PSR over a low-density grid with 32 × 32 partitions achieved a comparable accuracy with using a large-scale network for PSR over 256 × 256 partitions, but it had the benefit of reductions in network size and training time by 10 and 5 folds, respectively.

原文英語
文章編號3164
期刊Sensors
23
發行號6
DOIs
出版狀態已出版 - 16 03 2023

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© 2023 by the authors.

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