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

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 6; p. 3164
• Main Authors: Chan, Hsiao-Lung, Chang, Hung-Wei, Hsu, Wen-Yen, Huang, Po-Jung, Fang, Shih-Chin
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.03.2023; MDPI
• Subjects: Accuracy; Algorithms; Arrhythmias, Cardiac - diagnosis; Biometrics; Biometry; Cardiomyopathy; convolutional neural network; ECG biometric; Electrocardiogram; Electrocardiography; Electrocardiography - methods; Electrodes; Heart attacks; Heart failure; Heart Rate; Humans; individual identification; Machine learning; Morphology; Neural networks; Neural Networks, Computer; Noise; phase space reconstruction; Wavelet transforms; Taiwan; ECG biometric; phase space reconstruction; convolutional neural network; individual identification
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23063164

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.