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 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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