Electrocardiogram identification: Use a simple set of features in QRS complex to identify individuals
This paper presents a Multilayer Perception Neural Network developed to identify human subjects using electrocardiogram (ECG) signals. We use the amplitude values of Q, R and S as a features for our experiments. In this study, a total of 87 dataset were collected among 14 subjects from the Physik...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
Springer International Publishing
2016
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/60818/ http://irep.iium.edu.my/60818/ http://irep.iium.edu.my/60818/ http://irep.iium.edu.my/60818/9/60818-Electrocardiogram%20Identification-Use%20a%20simple.pdf http://irep.iium.edu.my/60818/8/60818-Electrocardiogram%20identification-SCOPUS.pdf |
| Summary: | This paper presents a Multilayer Perception Neural Network developed to
identify human subjects using electrocardiogram (ECG) signals. We use the
amplitude values of Q, R and S as a features for our experiments. In this study, a total
of 87 dataset were collected among 14 subjects from the Physikalisch-Technische
Bundesanstalt (PTB) database. Out of the 14 subjects, Q-R-S feature points were
taken from different day and time sessions to perform classification with MLP. Out
of this data, 66 % is used as training dataset while the remaining 34 % is used for
testing. Our method yields 96 % accuracy and demonstrates that the use of three
fiducial points is sufficient to identify a subject despite the common practice of
taking more feature points. |
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