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In this paper we suggest an approach to ECG data compression. The compression and reconstruction systems are implemented using multiple feedforward neural networks. By continuously monitoring the reconstruction error the compression system dynamically adapts to the local characteristics of the signal. Experimental results show that an average data rate of less than 100 bits/second can be sustained with a total energy error of about 0.5 percent.