Real time electrocardiogram identification with multi-modal machine learning algorithms
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 prese...
| Main Authors: | , , , , |
|---|---|
| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
Springer International Publishing
2017
|
| Subjects: | |
| Online Access: | http://irep.iium.edu.my/60819/ http://irep.iium.edu.my/60819/ http://irep.iium.edu.my/60819/1/60819_Real%20time%20electrocardiogram%20identification%20with%20multi-modal.pdf http://irep.iium.edu.my/60819/7/60819_Real%20time%20electrocardiogram%20identification%20with%20multi-modal_WOS.pdf |
| Summary: | 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. |
|---|