Single Heartbeat ECG Biometric Recognition using Convolutional Neural Network

Biometrics plays a crucial role in information security to identify and constantly validate individuals using physiological characteristics. During the last decade, Electrocardiogram (ECG) signal has emerged as a biometric modality due to its desirable characteristics for a reliable recognition system. However, the duration of the signal required for the recognition is long, and it is still one of the limitations of existing biometric recognition methods for their acceptability as a biometric modality. In this paper, a method is proposed to use the single heartbeat ECG signal for biometric recognition of a person with the help of deep machine learning technique. We investigate the use of a light and a pre-trained convolutional neural network for the classification of single heartbeat ECG signal segmented based on the R-peak and transformed used continuous wavelet transformation. Different scenarios of segmentations experimented; Fixed length, variable length, blind, and feature depending segmentations. The performance of the proposed method was tested with a landmark dataset available online. We obtained 99.94% and 99.83% recognition accuracy for a window of ECG signal for a single heartbeat outperforming existing methods.