Key result
Phase space reconstruction CNN achieves high ECG identification accuracy with a compact 32x32 architecture.
Why the study?
The effects of time delay and grid partition on the identification performance of phase space reconstruction-based ECG biometrics had not been investigated.
A PSR-based convolutional neural network can accurately identify individuals using ECG biometrics, and optimizing spatial mapping parameters can massively reduce computational load without compromising accuracy.
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.
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Chan et al. (2023) studied ECG biometric authentication (n=115). Phase Space Reconstruction-based Convolutional Neural Network (PSR-CNN) vs. Various time delays (2 to 36 ms) and grid partitions (16x16 to 256x256) was evaluated on Identification accuracy. The PSR-based convolutional neural network achieved high individual identification accuracy using an optimal time delay of 20 to 28 ms and a high-density grid partition, with a scaled-down 32x32 network offering comparable accuracy with reduced training time.
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