2025
doi: 10.12720/jait.16.1.101-108
SPP-AENet: A New ECG Biometric Identification Approach Based on Spatial Pyramid Pooling and Autoencoder
Xin Liu1
,
Di Wang2,3,* ,
Ping Wang1 ,
Tianyue Sun1 ,
Qi Sun1 ,
Yihan Fu2,3 ,
Yu Fu4
- 1Noncommissioned Officer Institute, Army Academy of Armored Forces, Changchun, China
- 2School of Electronic and Information Engineering, Tiangong University, Tianjin, China
- 3Tianjin Key Laboratory of Optoelectronic Detection Technology and System, Tianjin, China
- 4Computer Engineering Technical College (Artificial Intelligence College), Guangdong Polytechnic of Science and Technology, Zhuhai, China
- Manuscript receivedAugust 23, 2024
- revisedSeptember 23, 2024
- acceptedOctober 9, 2024
- publishedJanuary 15, 2025
Abstract
In recent years, Electrocardiogram (ECG) signals have emerged as a promising modality for biometric recognition, and have attracted significant attention. In this work, a new ECG biometric identification approach based on Spatial Pyramid Pooling (SPP) and Autoencoder (AE), referred to as the SPP-AENet, is proposed. The SPP-AENet is a hybrid architecture consisting of two parts: the SPP layer and AE layer. The SPP layer allows non-fixed length input and by incorporating prior knowledge, can ensure that critical characteristics of ECG signals are retained while compressing redundant information. The AE layer, through its automatic feature learning mechanism, can discover latent patterns and relationships within the data. The proposed method is evaluated using three public databases acquired under different health conditions, and gives a signal identification accuracy of 98.88%, 100%, and 100% for the ECG-ID, Physikalisch-Technische Bundesanstalt (PTB), and ST-Change database, respectively. To facilitate a better understanding of the model learning process, features of the SPP layer and the AE layer are visualized using t-distributed Stochastic Neighbor Embedding (t-SNE).
Keywords
- Electrocardiogram (ECG) biometric
- Spatial Pyramid Pooling (SPP)
- Autoencoder (AE)
- SPP-AENet
- t-distributed Stochastic Neighbor Embedding (t-SNE)
How to Cite
Xin Liu, Di Wang, Ping Wang, Tianyue Sun, Qi Sun, Yihan Fu, and Yu Fu, "SPP-AENet: A New ECG Biometric Identification Approach Based on Spatial Pyramid Pooling and Autoencoder," Journal of Advances in Information Technology, vol. 16, no. 1, pp. 101-108, 2025. https://doi.org/10.12720/jait.16.1.101-108
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
