Electrocardiogram identification: Use a simple set of features in QRS complex to identify individuals - , Tuerxunwaili; Mohd Nor, Rizal; Abdul Rahman, Abdul Wahab; Sidek, Khairul Azami; Ibrahim, Adamu Abubakar (Springer International Publishing, 2016)
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.