FPGA-based online-learning using parallel genetic algorithm and neural network for ECG signal classification

Author

Jewajinda, Yutana ; Chongstitvatana, Prabhas

Author_Institution

Nat. Electron. & Comput. Technol. Center, Nat. Sci. & Technol. Dev. Agency, Bangkok, Thailand

fYear

2010

fDate

19-21 May 2010

Firstpage

1050

Lastpage

1054

Abstract

This paper presents FPGA-based ECG signal classification based-on a parallel genetic algorithm and block-based neural network. The proposed parallel genetic algorithm has cellular-like structure which is suitable for hardware implementation. With online learning using hardware parallel genetic algorithm to block-based neural network, the complete ECG signal classification can be implemented in hardware. The proposed hardware can be implemented in FPGA or ASIC for a portable personalized ECG signal classifications for long term patient monitoring.

Keywords

electrocardiography; field programmable gate arrays; genetic algorithms; medical signal processing; neural nets; parallel algorithms; patient monitoring; signal classification; ASIC; ECG signal classification; FPGA based online learning; block based neural network; cellular like structure; parallel genetic algorithm; patient monitoring; portable personalized ECG signal classifications; Artificial neural networks; Computer networks; Electrocardiography; Evolutionary computation; Field programmable gate arrays; Genetic algorithms; Neural network hardware; Neural networks; Pattern classification; Programmable logic arrays;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical Engineering/Electronics Computer Telecommunications and Information Technology (ECTI-CON), 2010 International Conference on

Conference_Location

Chaing Mai

Print_ISBN

978-1-4244-5606-2

Electronic_ISBN

978-1-4244-5607-9

Type

conf

Filename

5491636