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Neural Computing and Applications

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01.06.2013 | Original Article

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

verfasst von: Yutana Jewajinda, Prabhas Chongstitvatana

Erschienen in: Neural Computing and Applications | Ausgabe 7-8/2013

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Abstract

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.

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Titel: A parallel genetic algorithm for adaptive hardware and its application to ECG signal classification
verfasst von: Yutana Jewajinda
Prabhas Chongstitvatana
Publikationsdatum: 01.06.2013
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 7-8/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-0963-9

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