title: ECG compression using long-term prediction.
abstract: A new algorithm for ECG signal compression is introduced. The compression system is based on the subautoregression (SAR) model, known also as the long-term prediction (LTP) model. The "periodicity" of the ECG signal is employed in order to further reduce redundancy, thus yielding high compression ratios. The suggested algorithm was evaluated using an in-house database. Very low bit rates on the order of 70 b/s are achieved with a relatively low reconstruction error (percent rms difference-PRD) of less than 10%. The algorithm was compared, using the same database, with the conventional linear prediction (short-term prediction--STP) method, and was found superior at any bit rate. The suggested algorithm can be considered a generalization of the recently published average beat subtraction method.
authors.affiliation: Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
authors.forename: G
authors.initials: G
authors.lastname: Nave
authors.forename: A
authors.initials: A
authors.lastname: Cohen
mesh_terms: D000465:Algorithms; D004562:Electrocardiography; D006331:Heart Diseases / Q000175:diagnosis; D006801:Humans; D016014:Linear Models; D015233:Models, Statistical; D010507:Periodicity; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted