What is the clinical evidence from this study?

Study design: Other. Population: Biometric authentication (n=427). Intervention: Self-supervised contrastive learning CNN vs. Unoptimized models or traditional supervised methods. Primary outcome: Authentication accuracy on PTBDB.

What does this research mean for the field?

A self-supervised contrastive learning CNN model achieves highly accurate and generalizable ECG-based biometric authentication across multiple datasets and can be effectively optimized for resource-constrained IoT edge sensors with minimal accuracy loss. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

September 9, 2024Open Access

ECG Biometric Authentication Using Self-Supervised Learning for IoT Edge Sensors

GWGuoxin WangUniversity College Dublin SSShanker ShreejithTrinity College Dublin ANAvishek NagUniversity College Dublin

Key Result

A self-supervised contrastive learning CNN achieved an ECG biometric authentication accuracy of 99.15% on the PTB database and over 98.5% on unseen datasets.

Structured PICO

Population

427 subjects from three public ECG databases (PTBDB, MITDB, ECGIDDB) used to evaluate an ECG-based biometric authentication algorithm.

Intervention

CNN-based biometric user authentication system using self-supervised contrastive learning, with model optimizations (quantization and pruning) for IoT edge sensors

Comparator

Unoptimized model

Outcome

Authentication accuracy

A CNN-based self-supervised contrastive learning model for ECG biometric authentication achieves high accuracy and can be optimized for efficient deployment on IoT edge sensors.

Limitations

Potential errors in QRS peak detection may result in losing authentication accuracy
Requires model compression techniques like quantization and pruning for deployment on resource-constrained IoT edge sensors

Abstract

Wearable Internet of Things (IoT) devices are gaining ground for continuous physiological data acquisition and health monitoring. These physiological signals can be used for security applications to achieve continuous authentication and user convenience due to passive data acquisition. This paper investigates an electrocardiogram (ECG) based biometric user authentication system using features derived from the Convolutional Neural Network (CNN) and self-supervised contrastive learning. Contrastive learning enables us to use large unlabeled datasets to train the model and establish its generalizability. We propose approaches enabling the CNN encoder to extract appropriate features that distinguish the user from other subjects. When evaluated using the PTB ECG database with 290 subjects, the proposed technique achieved an authentication accuracy of 99.15%. To test its generalizability, we applied the model to two new datasets, the MIT-BIH Arrhythmia Database and the ECG-ID Database, achieving over 98.5% accuracy without any modifications. Furthermore, we show that repeating the authentication step three times can increase accuracy to nearly 100% for both PTBDB and ECGIDDB. This paper also presents model optimizations for embedded device deployment, which makes the system more relevant to real-world scenarios. To deploy our model in IoT edge sensors, we optimized the model complexity by applying quantization and pruning. The optimized model achieves 98.67% accuracy on PTBDB, with 0.48% accuracy loss and 62.6% CPU cycles compared to the unoptimized model. An accuracy-vs-time-complexity tradeoff analysis is performed, and results are presented for different optimization levels.

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