Abstract
Patient adaptable ECG diagnosis algorithm is required and ECG based user recognition method is essential for patient adaptable smart healthcare system. For this, we developed ECG lead III signal based patient recognition model using artificial neural networks. To extract patient’s feature from ECG signal, we used three level noise removing method. After noise cancellation, number of vertices, signal intervals and detailed signal shapes were extracted from ECG signal as patient features. To show the validity of proposed model, we modeled recognition models for seven adults and we tested them under the artificial stress conditions including running, drinking and smoking and proposed model showed 92% of recognition accuracy rate.
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© 2005 Springer-Verlag Berlin Heidelberg
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Kim, T.S., Min, C.H. (2005). ECG Based Patient Recognition Model for Smart Healthcare Systems. In: Baik, DK. (eds) Systems Modeling and Simulation: Theory and Applications. AsiaSim 2004. Lecture Notes in Computer Science(), vol 3398. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30585-9_18
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DOI: https://doi.org/10.1007/978-3-540-30585-9_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24477-6
Online ISBN: 978-3-540-30585-9
eBook Packages: Computer ScienceComputer Science (R0)