, Volume 27, Issue 6, pp 603-611

Prognostic value of heart rate variability after myocardial infarction. A comparison of different data-processing methods

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Abstract

Reduced heart rate variability (HRV) has been reported as a predictor of mortality in recent myocardial infarction patients. However, its automated assessment in long-term ECG recordings is complicated by recording noise and beat-recognition errors which necessitate filtering of the computer-established sequence of beat-to-beat intervals, and visual checking and manual editing of the long-term recordings, making the whole method operator-dependent. To develop a fully automated method for analysis of HRV from 24 h ECG recordings, five filtering algorithms were combined with three methods of expressing HRV numerically and used to compare two groups of patients undergoing 24 h tape recordings of the ECG within the first two weeks after myocardial infarction. One group comprised 15 patients who later suffered death or ventricular tachycardia, the other group comprised 15 randomly selected uncomplicated cases. Using the same two groups of patients, three different methods of expressing HRV on a beat-to-beat basis were also compared empirically. The results show that alternative, operator-independent methods for establishing HRV from continuous long-term ECG recordings of postmyocardial infarction patients seem to be as effective as previously reported methods which rely on operator-dependent data post-processing techniques.