New human identification method using Tietze graph-based feature generation


Tuncer T., Aydemir E., DOĞAN Ş., Kobat M. A. , Kaya M. Ç. , Metin S.

Soft Computing, vol.25, no.21, pp.13437-13449, 2021 (Journal Indexed in SCI Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 25 Issue: 21
  • Publication Date: 2021
  • Doi Number: 10.1007/s00500-021-06094-5
  • Title of Journal : Soft Computing
  • Page Numbers: pp.13437-13449
  • Keywords: ECG signal classification, Tietze graph, Tunable Q-factor wavelet transform, Machine learning, BIOMETRIC RECOGNITION, NEURAL-NETWORK, ECG, CLASSIFICATION, SYSTEMS

Abstract

© 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.Electrocardiogram (ECG) signals have been widely used for disease diagnosis. Besides, the ECG signals can be used for human identification. In this work, a Tietze pattern and neighborhood component analysis (NCA)-based human identification method is proposed. Our model uses two feature generation methods to extract both statistical and textural features. The Tietze graph is considered to create a pattern of the presented local graph structure (LGS). Both statistical and textural feature generations are not enough to present a high-accurate model. Therefore, a multileveled structure must be created. Tunable Q-factor wavelet transform (TQWT) is employed as a decomposer. The generated/extracted features in each level are merged, and the merged features are selected using NCA. The k-nearest neighbors (kNN) classifier is deployed on the chosen features in the classification phase to obtain predicted values. The recommended method was tested on two ECG signal corpora called ECGID and MIT-BIH. The model achieved 99.12% and 99.94% accuracies on the used ECGID and MIT-BIH datasets, respectively.