ECG BIOMETRICS METHOD BASED ON CONVOLUTIONAL NEURAL NETWORK AND TRANSFER LEARNING

Personal identification based on ECG signals has been a significant challenge. The performance of an ECG authentication system depends significantly on the features extracted and the classifier subsequently applied. Although recently the deep neural networks based approaches featuring adaptive feature extractions and inherent classifications have attracted attention, they usually require a substantial set of training data. Aiming at tackling these issues, this paper presents a convolutional neural network-based transfer learning approach. It includes transferring the big data-trained GoogLeNet model into our identification task, fine-tuning the model using the 'finetune' idea, and adding three adaptive layers behind the original feature layer. The proposed approach not only requires a small set of training data, but also obtains great performance.