A new approach for ECG data compression is proposed in this paper. Using a nonlinear least squares optimization procedure, the approach employs an algorithm based on template model fitting. Only 12 parameters are required to fully represent the ECG signal without diagnostic information loss. The effectiveness of our ECG compression technique is described in terms of high compression ratios, relatively low distortion values of less than 9%, and a low computational cost, thus demonstrating the beneficial use of our technique for ECG data storage and online transmission. Comparisons with other recent compression methods in the literature have shown that our method performs better.

A noise tolerant template model technique for ECG feature extraction based on an individual-specific training approach is presented in this paper. Baseline wander, electrode motion artifacts, and electromyographic interference were added with varying signal-to-noise ratios (SNRs) to a dataset of approximately 3000 beats of different ECG recordings from the QT database to validate the performance of our technique. All of the QRS-complex, P-and T-waves detectors achieved an average sensitivity of 96.11%, positive predictivity of 83.8% and accuracy detection rate of 81.9% for SNRs between 24dB and -6dB, outperforming four recent beat detection algorithms evaluated with respect to the same types of noise. Furthermore, the ability of our technique to achieve efficient noise reduction including in-band noise, while preserving the morphological and clinical information of the original signal, is described.

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