Electrocardiogram identification based on data generative network and non-fiducial data processing

Nowadays, the use of biological signals as a criterion for identity recognition has gained increasing attention from various organizations and companies. Therefore, it has become crucial to have a biometric identity recognition method that is fast and accurate. In this paper, we propose a linear electrocardiogram (ECG) data preprocessing algorithm based on Kalman filters for rapid noise data filtering (wavelet transform filtering algorithm). Additionally, we introduce a generative network model called Data Generation Strategy Network (DRCN) based on generative networks. The DRCN is employed to augment training samples for convolutional classification networks, ultimately improving the classification performance of the model. Through the final experiments, our method successfully reduced the average misidentification rate of ECG-based identity recognition to 2.5%, and achieved an average recognition rate of 98.7% for each category, significantly surpassing previous achievements. In the future, this method is expected to be widely applied in the field of ECG-based identity recognition. •We propose a generative network-based strategy for electrocardiogram (ECG) data generation to address the issue of insufficient feature extraction by convolutional networks.•We propose a linear complexity filtering algorithm for preprocessing electrocardiogram (ECG) data.•This paper proposes a linear filtering operation on electrocardiogram signals to rapidly remove noise from the electrocardiogram.