title: Individual identification with high frequency ECG : preprocessing and classification by neural network.
abstract: In this research, we proposed that high frequency component of HFECG was applicable biometric feature for new identification system. We developed identification method by using neural network (NN), and aimed at the improvement of the classification rate. Preprocessing prior to NN is performed by justification on time axis and normalization on amplitude. As a result, an average of 99% classification rate was obtained from 9 subjects. We also made an attempt to identify in shorter time by shifting of the HFECG by a few samples to NN.
authors.affiliation: Tokyo City University, Masa Ishijima1.
authors.forename: Futoshi
authors.initials: F
authors.lastname: Tashiro
authors.forename: Takuya
authors.initials: T
authors.lastname: Aoyama
authors.forename: Toru
authors.initials: T
authors.lastname: Shimuta
authors.forename: Hiroki
authors.initials: H
authors.lastname: Ishikawa
authors.forename: Yuichi
authors.initials: Y
authors.lastname: Shimatani
authors.forename: Masa
authors.initials: M
authors.lastname: Ishijima
authors.forename: Masaki
authors.initials: M
authors.lastname: Kyoso
mesh_terms: D004562:Electrocardiography / Q000379:methods*; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted