CALVIN: A Rule Based Expert System for Improving Arrhymia Detector Performance During Noisy ECGS

Abstract

Human experts far outperform automated arrhythmia detectors in analyzing ECG data corrupted by noise and artifact. Humans make use of considerable a priori knowledge about cardiac electrophysiology and knowledge acquired from the specific ECG under analysis. R-R interval, coupling intervals of ectopic beats, and commonly occurring beat patterns observed during noise-free ECG segments form a knowledge base which is used in accurately detecting and classifying true QRS complexes in the presence of severe noise.