HeartID: A Multiresolution Convolutional Neural Network for ECG-Based Biometric Human Identification in Smart Health Applications - Zhang, Qingxue; Zhou, Dian; Zeng, Xuan (IEEE access, 2017)
Body area networks, including smart sensors, are widely reshaping health applications in the new era of smart cities. To meet increasing security and privacy requirements, physiological signalbased biometric human identification is gaining tremendous attention. This paper focuses on two major impediments: the signal processing technique is usually both complicated and data-dependent and the feature engineering is time-consuming and can fit only specific datasets . To enable a data-independent and highly generalizable signal processing and feature learning process, a novel wavelet domain multiresolution convolutional neural network is proposed. Specifically, it allows for blindly selecting a physiological signal segment for identification purpose, avoiding the complicated signal fiducial characteristics extraction process. To enrich the data representation, the random chosen signal segment is then transformed to the wavelet domain, where multiresolution time-frequency representation is achieved. An auto-correlation operation is applied to the transformed data to remove the phase difference as the result of the blind segmentation operation. Afterward, a multiresolution 1-D-convolutional neural network (1-D-CNN) is introduced to automatically learn the intrinsic hierarchical features from the wavelet domain raw data without datadependent and heavy feature engineering, and perform the user identification task. The effectiveness of the proposed algorithm is thoroughly evaluated on eight electrocardiogram datasets with diverse behaviors, such as with or without severe heart diseases, and with different sensor placement methods. Our evaluation is much more extensive than the state-of-the-art works, and an average identification rate of 93.5% is achieved. The proposed multiresolution 1-D-CNN algorithm can effectively identify human subjects, even from randomly selected signal segments and without heavy feature engineering. This paper is expected to demonstrate the feasibility and effectiveness of applying the blind signal processing and deep learning techniques to biometric human identification, to enable a low algorithm engineering effort and also a high generalization ability.