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F Geheb

Publications and source records attributed to F Geheb.

2 recordsLinked to original sources
PubMedDate not supplied

Automated testing of arrhythmia monitors using annotated databases.

Arrhythmia-algorithm performance is typically tested using the AHA and MIT/BIH databases. The tools for this test are simulation software programs. While these simulations provide rapid results, they neglect hardware and software effects in the monitor. To provide a more accurate measure of performance in the actual monitor, a system has been developed for automated arrhythmia testing. The testing system incorporates an IBM-compatible personal computer, a digital-to-analog converter, an RS232 board, a patient-simulator interface to the monitor, and a multi-tasking software package for data conversion and communication with the monitor. This system "plays" patient data files into the monitor and saves beat classifications in detection files. Tests were performed using the MIT/BIH and AHA databases. Statistics were generated by comparing the detection files with the annotation files. These statistics were marginally different from those that resulted from the simulation. Differences were then examined. As expected, the differences were related to monitor hardware effects.

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PubMedDate not supplied

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.

Algorithms↗