Critique of arrhythmia detectors based on heuristic rules.

Every arrhythmia detector employs a beat classifier to discriminate between normal (N) and ventricular (V) beats. In most of these beat-classification algorithms, a set of rules is employed to distinguish between N and V beats using a common set of features extracted from the real-time ECG signal and/or correlation of QRS complexes with the dominant QRS template. A common set of these features includes: beat area, beat width, beat amplitude, beat polarity, and R-to-R interval. Heuristic methods are commonly used to adapt the rules to particular databases. These classifiers are rule-based classifiers that employ AND-OR binary structures and hand-tuned thresholds for making decisions in the feature space. The complexity of the feature space increases as the number of features increases. For k features, a k-dimensional space is required. Thus, the separation between N and V space distributions becomes more difficult, especially since these distributions overlap. When AND-OR binary structures with hand-tuned thresholds or linear-separation techniques are used to separate N and V distributions in a k-dimensional feature space, errors are guaranteed, because these distributions are not linearly separable. As a results, these algorithms have limited dynamic ranges. This means that the sensitivity for a certain class of beats (N or V) will grow only at the expense of positive predictivity for that class, and vice versa.