New human identification method using Tietze graph-based feature generation
Yükleniyor...
Tarih
2021
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Anahtar Kelimeler
ECG signal classification, Tietze graph, Tunable Q-factor wavelet transform, Machine learning
Kaynak
Soft Computing
WoS Q Değeri
Q2
Scopus Q Değeri
Q2
Cilt
Sayı
Künye
Tuncer, T., Aydemir, E., Dogan, S., Kobat, M. A., Kaya, M. C., & Metin, S. (2021). New human identification method using Tietze graph-based feature generation. Soft Computing, 1-13.
