Minimal Preprocessing of ECG Signals for Deep Learning-Based Biometric Systems

In recent years, subject identification through electrocardiograms (ECGs) broaden the possibilities of existing biometric systems. In this study, we proposed a novel ECG-based biometric identification method designed to be computationally inexpensive, while being sufficiently accurate for a broad variety of application contexts. Specifically, we adapted an established deep learning model known as Deep-ECG to process raw ECG data with minimal preprocessing. We examined the robustness of the model by investigating the identification accuracy across three experiments, obtaining results comparable to more complex state-of-The-Art methods. For all experiments, we utilized the SHAREE dataset, containing 24h Holter recordings form 139 subjects, collected in uncontrolled conditions and trained the network by randomly selecting ECG segments during daytime. In the first experiment, we quantified the performance by varying the number of subjects to identify and the number of ECG leads concurrently fed in input. In the second experiment, we varied the number of training samples per individual and the duration of the ECG segments. In the third experiment, we reimplemented the original pipeline of the Deep-ECG model to compare the performance with the new approach with minimal preprocessing. We obtained that the new approach achieved similar performance to the original Deep-ECG model. Also, the new approach obtained accuracies > 80% for individual leads and > 90% for multiple leads when using ECG segments of 2 seconds. Using this ECG duration, the minimal number of training samples per individual to achieve an accuracy > 80% was 100. Our study showed that the computational cost of the Deep-ECG model could significantly be improved by changing the pipeline previously proposed with another one with minimal preprocessing. The source code replicating the results of this study is available on GitHub.