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ERIC Number: ED571582
Record Type: Non-Journal
Publication Date: 2016-Dec
Pages: 8
Abstractor: As Provided
Reference Count: 21
ECG Identification System Using Neural Network with Global and Local Features
Tseng, Kuo-Kun; Lee, Dachao; Chen, Charles
International Association for Development of the Information Society, Paper presented at the International Conferences on Internet Technologies & Society (ITS), Education Technologies (ICEduTECH), and Sustainability, Technology and Education (STE) (Melbourne, Australia, Dec 6-8, 2016)
This paper proposes a human identification system via extracted electrocardiogram (ECG) signals. Two hierarchical classification structures based on global shape feature and local statistical feature is used to extract ECG signals. Global shape feature represents the outline information of ECG signals and local statistical feature extracts the information between signals in time domain. Genetic algorithm based back propagation neural network is used as the specific classifier. Experiment results show that our identification system can achieves an average 97.6% accuracy on a 38 subjects of PTB public ECG database and an average 100% accuracy on an 18 subjects of MIT-BIH public ECG database, which demonstrates the proposed system can reach satisfactory effects. [For full proceedings, see ED571459.]
International Association for the Development of the Information Society. e-mail:; Web site:
Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A