Low Complexity ECG Biometric Authentication for IoT Edge Devices

Wearable Internet of Things (IoT) devices are getting ubiquitous for continuous physiological data acquisition and health monitoring. This paper investigates an electrocardiogram (ECG) based biometric user authentication technique for IoT edge devices. A convolutional neural network (CNN) based deep learning technique for user authentication is proposed. The proposed technique achieves an authentication accuracy of 99.63% when tested with 290 subjects from Physionet PTB ECG database. To limit the complexity of the technique for IoT edge nodes, we applied optimisation techniques such as binarisation and approximation of the CNN weights. Accuracy-vs-time-complexity trade-off analysis is performed and results are presented for different optimisations. Our evaluations shows that the complexity-optimised method achieves 98.88% authentication accuracy with acceptable CPU cycles consumed.