title: Normalizing electrocardiograms of both healthy persons and cardiovascular disease patients for biometric authentication.
abstract: Although electrocardiogram (ECG) fluctuates over time and physical activity, some of its intrinsic measurements serve well as biometric features. Considering its constant availability and difficulty in being faked, the ECG signal is becoming a promising factor for biometric authentication. The majority of the currently available algorithms only work well on healthy participants. A novel normalization and interpolation algorithm is proposed to convert an ECG signal into multiple template cycles, which are comparable between any two ECGs, no matter the sampling rates or health status. The overall accuracies reach 100% and 90.11% for healthy participants and cardiovascular disease (CVD) patients, respectively.
authors: Shenzhen Institutes of Advanced Technology, and Key Lab for Health Informatics, Chinese Academy of Sciences, Shenzhen, Guangdong, P.R. China.
authors: Meixue
authors: M
authors: Yang
authors: Bin
authors: B
authors: Liu
authors: Miaomiao
authors: M
authors: Zhao
authors: Fan
authors: F
authors: Li
authors: Guoqing
authors: G
authors: Wang
authors: Fengfeng
authors: F
authors: Zhou
mesh_terms: D000465:Algorithms; D056667:Biometric Identification; D002318:Cardiovascular Diseases / Q000000981:diagnostic imaging*; D004562:Electrocardiography / Q000592:standards*; D006262:Health; D006801:Humans; D012015:Reference Standards; D012815:Signal Processing, Computer-Assisted; D014463:Ultrasonography
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