A parallel genetic algorithm for adaptive hardware and its application to ECG signal classification

This paper presents a parallel genetic algorithm (GA) called the cellular compact genetic algorithm (c-cGA) and its implementation for adaptive hardware. An adaptive hardware based on the c-cGA is proposed to automate real-time classification of ECG signals. The c-cGA not only provides a strong sear...

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Bibliographic Details
Published in:Neural computing & applications Vol. 22; no. 7-8; pp. 1609 - 1626
Main Authors: Jewajinda, Yutana, Chongstitvatana, Prabhas
Format: Journal Article
Language:English
Published: London Springer-Verlag 01.06.2013
Springer
Subjects:
Applied sciences
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Computer science; control theory; systems
Data Mining and Knowledge Discovery
Exact sciences and technology
Image Processing and Computer Vision
Inference from stochastic processes; time series analysis
Learning and adaptive systems
Mathematics
Multivariate analysis
Original Article
Parametric inference
Probability and statistics
Probability and Statistics in Computer Science
Sciences and techniques of general use
Statistics
ECG signal classification
Parallel genetic algorithm
Adaptive hardware
Parallel algorithm
Neural computation
Adaptive algorithm
Adaptive system
Field programmable gate array
Long term variation
Adaptive filter
Neural network
Real time
Long term
Implementation
Learning
Digital filter
Genetic algorithm
Classification
Genetics
Monitoring
ISSN:0941-0643, 1433-3058
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Summary:This paper presents a parallel genetic algorithm (GA) called the cellular compact genetic algorithm (c-cGA) and its implementation for adaptive hardware. An adaptive hardware based on the c-cGA is proposed to automate real-time classification of ECG signals. The c-cGA not only provides a strong search capability while maintaining genetic diversity using multiple GAs but also has a cellular-like structure and is a straight-forward algorithm suitable for hardware implementation. The c-cGA hardware and an adaptive digital filter structure also perform an adaptive feature selection in real time. The c-cGA is applied to a block-based neural network (BbNN) for online learning in the hardware. Using an adaptive hardware approach based on the c-cGA, an adaptive hardware system for classifying ECG signals is feasible. The proposed adaptive hardware can be implemented in a field programmable gate array (FPGA) for an adaptive embedded system applied to personalised ECG signal classifications for long-term patient monitoring.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-012-0963-9