Single Heartbeat ECG Authentication: a 1D-CNN Framework for Robust and Efficient Human Identification

This study proposes a small one-dimensional convolutional neural network (1D-CNN) framework for individual authentication, considering the hypothesis that a single heartbeat as input is sufficient to create a robust system. A short segment between R to R of electrocardiogram (ECG) signals was chosen to generate single heartbeat samples by enforcing a rigid length thresholding procedure combined with an interpolation technique. Additionally, we explored the benefits of the synthetic minority oversampling technique (SMOTE) to tackle the imbalance in sample distribution among individuals. The proposed framework was evaluated individually and in a mixture of four public databases: MIT-BIH Normal Sinus Rhythm (NSRDB), MIT-BIH Arrhythmia (MIT-ARR), ECG-ID, and MIMIC-III which are available in the Physionet repository. The proposed framework demonstrated excellent performance, achieving a perfect score (100%) across all metrics (i.e., accuracy, precision, sensitivity, and F1-score) on individual NSRDB and MIT-ARR databases. Meanwhile, the performance remained high, reaching more than 99.6% on mixed datasets that contain larger populations and more diverse conditions. The impressive performance demonstrated in both small and large subject groups emphasizes the model's scalability and potential for widespread implementation, particularly in security contexts where timely authentication is crucial. For future research, we need to examine the incorporation of multimodal biometric systems and extend the applicability of the framework to real-time environments and larger populations.