FPGA-based system for artificial neural network arrhythmia classification

The automatic detection and cardiac classification are essential tasks for real-time cardiac diseases diagnosis. In this context, this paper describes a field programmable gates array (FPGA) implementation of arrhythmia recognition system, based on artificial neural network. Firstly, we have develop...

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Bibliographic Details
Published in:Neural computing & applications 2020-04, Vol.32 (8), p.4105-4120
Main Authors: Zairi, Hadjer, Kedir Talha, Malika, Meddah, Karim, Ould Slimane, Saliha
Format: Article
Language:English
Subjects:
Arrhythmia
Artificial Intelligence
Artificial neural networks
Cardiac arrhythmia
Classification
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Diagnostic software
Diagnostic systems
Electrocardiography
Energy conservation
Energy consumption
Field programmable gate arrays
Floating point arithmetic
Image Processing and Computer Vision
Monitoring
Neural networks
Original Article
Performance evaluation
Probability and Statistics in Computer Science
Real time
Telemedicine
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Summary:The automatic detection and cardiac classification are essential tasks for real-time cardiac diseases diagnosis. In this context, this paper describes a field programmable gates array (FPGA) implementation of arrhythmia recognition system, based on artificial neural network. Firstly, we have developed an optimized software-based medical diagnostic approach, capable of defining the best electrocardiogram (ECG) signal classes. The main advantage of this approach is the significant features minimization, compared to the existing researches, which leads to minimize the FPGA prototype size and saving energy consumption. Secondly, to provide a continuous and mobile arrhythmia monitoring system for patients, we have performed a hardware implementation. The FPGA has been referred due to their easy testing and quick implementation. The optimized approach implementation has been designed on the Nexys4 Artix7 evaluation kit using the Xilinx System Generator for DSP. In order to evaluate the performance of our proposal system, the classification performances of proposed FPGA fixed point have been compared to those obtained from the MATLAB floating point. The proposed architecture is validated on FPGA to be a customized mobile ECG classifier for long-term real-time monitoring of patients.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-019-04081-4