Fragmented QRS in prediction of cardiac deaths and heart failure hospitalizations after myocardial infarction.

BACKGROUND: Increased QRS fragmentation in visual inspection of 12-lead ECG has shown association with cardiac events in postmyocardial infarction (MI) patients. We investigated user-independent computerized intra-QRS fragmentation analysis in prediction of cardiac deaths and heart failure (HF) hospitalizations after MI. METHODS: Patients (n = 158) with recent MI and reduced left ventricular ejection fraction (LVEF) were studied. A 120-lead body surface potential mapping was performed at hospital discharge. Intra-QRS fragmentation was computed as the number of extrema (fragmentation index FI) in QRS. QRS duration (QRSd) was computed for comparison. RESULTS: During a mean follow-up of 50 months 15 patients suffered cardiac death and 23 were hospitalized for HF. Using the mean + 1 SD as cut-point both parameters were univariate predictors of both end-points. In multivariate analysis including age, gender, LVEF, previous MI, bundle branch block, atrial fibrillation, and diabetes FI was an independent predictor for cardiac deaths (HR 8.7, CI 3.0-25.6) and HF hospitalizations (HR 3.8, CI 1.6-9.3) whereas QRSd only predicted HF hospitalizations (HR 4.6, CI 2.0-10.7). In comparison to QRSd, FI showed better positive (PPA) and equal negative (NPA) predictive accuracy for both end-points, and PPA was further improved when combined to LVEF < 40%. Limiting fragmentation analysis to 12-lead ECG or a randomly selected 8-lead set instead of all 120 leads resulted in an almost similar prediction. CONCLUSIONS: Increased QRS fragmentation in post-MI patients predicts cardiac deaths and HF progression. A computer-based fragmentation analysis is a stronger predictor than QRSd.

Robust denoising of electrophoresis and mass spectrometry signals with minimum description length principle.

The need for high-throughput assays in molecular biology places increasing requirements on the applied signal processing and modelling methods. In order to be able to extract useful information from the measurements, the removal of undesirable signal characteristics such as random noise is required. This can be done in a quite elegant and efficient way by the minimum description length (MDL) principle, which treats and separates 'noise' from the useful information as that part in the data that cannot be compressed. In its current form the MDL denoising method assumes the Gaussian noise model but does not require any ad hoc parameter settings. It provides a basis for high-speed automated processing systems without requiring continual user interventions to validate the results as in the conventional signal processing methods. Our analysis of the denoising problem in mass spectrometry, capillary electrophoresis genotyping, and sequencing signals suggests that the MDL denoising method produces robust and intuitively appealing results sometimes even in situations where competing approaches perform poorly.