A Scalable ECG Identification System Based on Locality-Sensitive Hashing.

Electrocardiograms (ECGs) have the inherent property of being intrinsic and dynamic and are shown to be unique among individuals, making them promising as a biometric trait. Although many ECG biometric recognition approaches have demonstrated accurate recognition results in small enrollment sets, they can suffer from performance degradation when many subjects are enrolled. This study proposes an ECG biometric identification system based on locality-sensitive hashing (LSH) that can accommodate a large number of registrants while maintaining satisfactory identification accuracy.

ECG-based Biometric Recognition without QRS Segmentation: A Deep Learning-Based Approach.

Electrocardiogram (ECG)-based identification systems have been widely studied in the literature. Usually, an ECG trace needs to be segmented according to the detected R peaks to enable feature extraction from the ECGs of duration equal to nearly one cardiac cycle. Beat averaging should also be applied to reduce the influence of inter-beat variation on the extracted features and identification accuracy.

A Scalable Open-Set ECG Identification System Based on Compressed CNNs.

Deep learning (DL) is known for its excellence in feature learning and its ability to deliver high-accuracy results. Its application to ECG biometric recognition has received increasing interest but is also accompanied by several deficiencies. In this study, we focus on applying DL, especially convolutional neural networks (CNNs), to ECG biometric identification to address these deficiencies.