Real time electrocardiogram identification with multi-modal machine learning algorithms
Yükleniyor...
Dosyalar
Tarih
2018
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer International Publishing AG
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Weaknesses in conventional identification technologies such as identification cards, badges and RFID tags prompts attention to biometric form of identification. Biometrics like voice, brain signal and finger print are unique human traits that can be used for identification. In this paper we present an identification system based on Electrocardiogram (heart signal). There is a considerable number of research in the past with high accuracy for identification , however, most ignore the practical time required to identify an individual. In this study, we explored a more practical approach in identification by reducing the number of time required for identification. We explore ways to identity a person within 3-4 s using just 5 heart beats. We extracted few reliable features from each QRS complexes, combined effort of three algorithms to achieve 96% accuracy. This approach is more suitable and practical in real time applications where time for identification is important.
Açıklama
Anahtar Kelimeler
SVM, Random forest, Logistic regression, QRS complex, ECG biometric, Identification
Kaynak
2nd International Conference of Reliable Information and Communication Technology (IRICT)
WoS Q Değeri
N/A
Scopus Q Değeri
Q3
Cilt
5
Sayı
Künye
Waili, T., Nor, R. M., Sidek, K. A., Rahman, A. W. B.A. & Güven, G. (2018). Real time electrocardiogram identification with multi-modal machine learning algorithms. 2nd International Conference of Reliable Information and Communication Technology (IRICT), 5, 459-466. doi:10.1007/978-3-319-59427-9_48
