Key result
Three-layer artificial neural network algorithm extracts and encodes ECG features for Holter data compression.
Artificial neural networks can be utilized for data compression and feature extraction of ECG waveforms in digital Holter monitors.
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May enable efficient long-term ECG storage in Holter devices; leaves open prospective validation of diagnostic fidelity before clinical use.
A data-compression algorithm for digital Holter recording using artificial neural networks (ANNs) is described. A three-layer ANN that has a hidden layer with a few units is used to extract features of the ECG (electrocardiogram) waveform as a function of the activation levels of the hidden layer units. The number of output and input units is the same. The backpropagation algorithm is used for learning. The network is tuned with supervised signals that are the same as the input signals. One network (network 1) is used for data compression and another (network 2) is used for learning with current signals. Once the network is tuned, the common waveform features are encoded by the interconnecting weights of the network. The activation levels of the hidden units then express the respective features of the waveforms for each consecutive heartbeat.
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Iwata et al. (1990) studied this question. Data-compression algorithm using artificial neural networks was evaluated. A data-compression algorithm for digital Holter recording using a three-layer artificial neural network was described to extract and encode features of the ECG waveform.
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