ADAPTIVE ESTIMATION OF QRS COMPLEX WAVE FEATURES OF ECG SIGNAL BY THEHERMITE MODEL

The most characteristic wave set in ECG signals is the ORS complex. Au tomatic procedures to classify the ORS are very useful in the diagnosi s of cardiac dysfunctions. Early detection and classification of ORS c hanges are important in realtime monitoring. ECG data compression is a lso important for storage and data transmission. An Adaptive Hermite M odel Estimation System (AHMES) is presented for on-line beat-to-beat e stimation of the features that describe the ORS complex with the Hermi te model. The AHMES is based on the multiple-input adaptive linear com biner, using as inputs the succession of the QRS complexes and the Her mite functions, where a procedure has been incorporated to adaptively estimate a width related parameter b. The system allows an efficient r eal-time parameter extraction for classification and data compression. The performance of the AHMES is compared with that of direct feature estimation, studying the improvement in signal-to-noise ratio. In addi tion, the effect of misalignment at the QRS mark is shown to become a neglecting low-pass effect. The results allow the conditions in which the AHMES improves the direct estimate to be established. The applicat ion is shown, for subsequent classification, of the AHMES in extractin g the ORS features of an ECG signal with the bigeminy phenomena. Anoth er application is highlighted that helps wide ectopic beats detection using the width parameter b.