Machine Learning-Empowered Biometric Methods for Biomedicine

AIMS Medical Science

article

Qingxue Zhang¹  Dian Zhou¹  Xuan Zeng¹ 

[1] Department of Electrical Engineering, University of Texas at Dallas;Department of Microelectronics, Fudan University

关键词: biomedicine;    smart health;    biometrics;    user identification;    electrocardiogram;    electroencephalogram;    neural network;    random forest;    AdaBoost;    ;   

DOI  :  10.3934/medsci.2017.3.274

来源: American Institute of Mathematical Sciences

【 摘 要 】

Nowadays, pervasive computing technologies are paving a promising way for advanced smart health applications. However, a key impediment faced by wide deployment of these assistive smart devices, is the increasing privacy and security issue, such as how to protect access to sensitive patient data in the health record. Focusing on this challenge, biometrics are attracting intense attention in terms of effective user identification to enable confidential health applications. In this paper, we take special interest in two bio-potential-based biometric modalities, electrocardiogram (ECG) and electroencephalogram (EEG), considering that they are both unique to individuals, and more reliable than token (identity card) and knowledge-based (username/password) methods. After extracting effective features in multiple domains from ECG/EEG signals, several advanced machine learning algorithms are introduced to perform the user identification task, including Neural Network, K-nearest Neighbor, Bagging, Random Forest and AdaBoost. Experimental results on two public ECG and EEG datasets show that ECG is a more robust biometric modality compared to EEG, leveraging a higher signal to noise ratio and also more distinguishable morphological patterns. Among different machine learning classifiers, the random forest greatly outperforms the others and owns an identification rate as high as 98%. This study is expected to demonstrate that properly selected biometric empowered by an effective machine learner owns a great potential, to enable confidential biomedicine applications in the era of smart digital health.

【 授权许可】

CC BY