Lossless compression schemes for ECG signals using neural network predictors
EURASIP Journal on Applied Signal Processing
Evolving a Bayesian classifier for ECG-based age classification in medical applications
Applied Soft Computing
ECG signal compression using 2D wavelet foveation
Proceedings of the 2009 International Conference on Hybrid Information Technology
Active learning methods for electrocardiographic signal classification
IEEE Transactions on Information Technology in Biomedicine
Modelling and Simulation in Engineering
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The method of truncated singular value decomposition (SVD) is proposed for electrocardiogram (ECG) data compression. The signal decomposition capability of SVD is exploited to extract the significant feature components of the ECG by decomposing the ECG into a set of basic patterns with associated scaling factors. The signal information is mostly concentrated within a certain number of singular values with related singular vectors due to the strong interbeat correlation among ECG cycles. Therefore, only the relevant parts of the singular triplets need to be retained as the compressed data for retrieving the original signals. The insignificant overhead can be truncated to eliminate the redundancy of ECG data compression. The Massachusetts Institute of Technology-Beth Israel Hospital arrhythmia database was applied to evaluate the compression performance and recoverability in the retrieved ECG signals. The approximate achievement was presented with an average data rate of 143.2 b/s with a relatively low reconstructed error. These results showed that the truncated SVD method can provide efficient coding with high-compression ratios. The computational efficiency of the SVD method in comparing with other techniques demonstrated the method as an effective technique for ECG data storage or signals transmission.