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.