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K Hnatkova

Publications and source records attributed to K Hnatkova.

58 records · Page 4Linked to original sources
PubMed1994

Influence of filtering techniques on the time-domain analysis, diagnosis, and clinical use of signal-averaged electrocardiogram.

In order to investigate the effect of different filtering techniques on the time-domain analysis of signal-averaged electrocardiogram (SAECG), recordings of 1,192 subjects were analyzed using Butterworth and Del Mar filters, both set at 40-250 Hz high and low pass frequencies. The recordings were taken from six clinically defined groups: (a) survivors of acute myocardial infarction (n = 553); (b) patients with sustained symptomatic postinfarction ventricular tachycardia (n = 89); (c) patients with hyperthropic cardiomyopathy (n = 219); (d) patients with dilated cardiomyopathy (n = 76); (e) direct relatives of patients with dilated cardiomyopathy (n = 170); and (f) normal healthy volunteers (n = 85). The study investigated differences between the SAECG results reported with both filters in three individual aspects: (1) numerical values of individual time-domain SAECG variables; (2) differences in SAECG findings of patients with postinfarction ventricular tachycardia and pair matched patients with uncomplicated follow-up after acute infarction; and (3) the power of SAECG findings to predict high risk of arrhythmic complication (sudden death and/or sustained ventricular tachycardia) among survivors of acute myocardial infarction. Compared with the Butterworth filter, the Del Mar filter led to a systematic difference of +8% in total QRS duration, was equally powerful in distinguishing between the pair matched patients with and without postinfarction ventricular tachycardia, and was statistically significantly more powerful in identifying those survivors of acute infarction who were at high risk of arrhythmic complications. The study concludes that the use of different filters may produce discordant results of SAECG analysis. Normal and abnormal values for various types of SAECG recording and analysis have to be established individually for different equipment and different software settings. These optimal cut-offs of SAECG variables should also take into account the clinical characteristics of patient groups.

Cardiomyopathy, Dilated↗
PubMed1994

Adjustment of QT dispersion assessed from 12 lead electrocardiograms for different numbers of analysed electrocardiographic leads: comparison of stability of different methods.

OBJECTIVE: Normal electrocardiographic recordings were analysed to establish the influence of measurement of different numbers of electrocardiographic leads on the results of different formulas expressing QT dispersion and the effects of adjustment of QT dispersion obtained from a subset of an electrocardiogram to approximate to the true QT dispersion obtained from a complete electrocardiogram. SUBJECTS AND METHODS: Resting 12 lead electrocardiograms of 27 healthy people were investigated. In each lead, the QT interval was measured with a digitising board and QT dispersion was evaluated by three formulas: (A) the difference between the longest and the shortest QT interval among all leads; (B) the difference between the second longest and the second shortest QT interval; (C) SD of QT intervals in different leads. For each formula, the "true" dispersion was assessed from all measurable leads and then different combinations of leads were omitted. The mean relative differences between the QT dispersion with a given number of omitted leads and the "true" QT dispersion (mean relative errors) and the coefficients of variance of the results of QT dispersion obtained when omitting combinations of leads were compared for the different formulas. The procedure was repeated with an adjustment of each formula dividing its results by the square root of the number of measured leads. The same approach was used for the measurement of QT dispersion from the chest leads including a fourth formula (D) the SD of interlead differences weighted according to the distances between leads. For different formulas, the mean relative errors caused by omitting individual electrocardiographic leads were also assessed and the importance of individual leads for correct measurement of QT dispersion was investigated. RESULTS: The study found important differences between different formulas for assessment of QT dispersion with respect to compensation for missing measurements of QT interval. The standard max-min formula (A) performed poorly (mean relative errors of 6.1% to 18.5% for missing one to four leads) but was appropriately adjusted with the factor of 1/square root of n (n = number of measured leads). In a population of healthy people such an adjustment removed the systematic bias introduced by missing leads of the 12 lead electrocardiogram and significantly reduced the mean relative errors caused by the omission of several leads. The unadjusted SD was the optimum formula (C) for the analysis of 12 lead electrocardiograms, and the weighted standard deviation (D) was the optimum for the analysis of six lead chest electrocardiograms. The coefficients of variance of measurements of QT dispersion with different missing leads were very large (about 3 to 7 for one to four missing leads). Independently of the formula for measurement of QT dispersion, omission of different leads produced substantially different relative errors. In 12 lead electrocardiograms the largest relative errors (> 10%) were caused by omitting lead aVL or lead V1. CONCLUSIONS: Because of the large coefficients of variance, the concept of adjusting the QT dispersion for different numbers of electrocardiographic leads used in its assessment is difficult if not impossible to fulfil. Thus it is likely to be more appropriate to assess QT dispersion from standardised constant sets of electrocardiographic leads.

Adult↗
PubMed1993

Long-term reproducibility of individual indices of time-domain analysis, spectral temporal mapping, and spectral turbulence analysis of signal-averaged ECGs.

This study compared short-term (1 day) and long-term (1 week and 1 month) reproducibility of the numerical results of 21 individual indices of time-domain analysis, spectral temporal mapping, and spectral turbulence analysis of the signal-averaged electrocardiogram in 28 healthy volunteers (18 men, 10 women, mean age, 31 years; range, 22-39 years). For each of the 21 indices and for each of the repeated recordings (ie, recordings on day 1, 7, and 30), the mean relative error versus recording of day 0 was calculated together with the rank correlation coefficient. The study showed that (1) reproducibility of time-domain analysis with the 40-Hz high-pass filter was superior to that of time-domain analysis performed with the 25-Hz high-pass filter, (2) reproducibility of time-domain variables was similar to that of spectral turbulence analysis of the entire QRS complex and was superior to that of spectral temporal mapping, (3) reproducibility of frequency-domain techniques not based on time-domain measurements (such as spectral turbulence analysis of the entire QRS complex) was significantly better than that of frequency-domain techniques combined with time-domain measurements (such as spectral turbulence analysis of the low-power terminal QRS region or spectral temporal mapping), and (4) short- and long-term reproducibility of signal-averaged electrocardiographic indices is similar in a healthy population.

Adult↗