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The objective of this study was to evaluate the beat-to-beat variability of the QT interval during the day and night. A new algorithm was used to detect the onset of the QRS, the apex of the T wave, and end of the T in ambulatory electrocardiographic recordings. Beat-to-beat variability of QT, QaT, and QTc during the day and night was studied in the time, frequency, and chaotic domains. Participants were adults without clinical evidence of heart disease. Although the QT duration was higher (p = 0.0001) at night, the beat-to-beat variability of this interval was lower: in the time domain (decreased standard deviation, p = 0.0005), in the frequency domain (decreased low-frequency power of the spectra, p = 0.004), and the chaotic domain (tighter clustering of the points in the Poincaré plots). The high-frequency to low-frequency ratio of the power spectra of the QT (and the RR) was higher (p = 0.03) at night. Beat-to-beat QT variability in the time, frequency, and chaotic domains is decreased at night with shift of the QT modulation to higher frequencies corresponding to respiration and representing vagal preponderance. The techniques presented here and the findings in normal subjects may be useful in evaluating the risk for arrhythmic events in patients with heart disease.

Background: Previous studies have suggested that irregular T wave morphologies are associated with an increased risk of sudden death. However, automated characterization of T wave abnormalities has been hampered by the lack of suitable analysis techniques. In this study, we tested a new method of T wave analysis in German shepherd dogs with inherited ventricular arrhythmias and sudden death. Methods: Sets of 24-hour ambulatory ECG recordings obtained from unaffected (n = 6) and affected (n = 5) dogs were digitized, automatically annotated to label each R wave, and placed into a matrix with the R waves aligned. A vector quantization algorithm separated the QRS-T complexes into classes according to T wave morphology. The existence of notched T waves was determined by assessing the number of zero crossings of the first derivative during the T wave. Results: The duration of the QT interval was similar in affected and unaffected dogs (182 ? 14 ms vs 176 5 16 ms, respectively). However, T wave morphology differed between the two groups. Specifically, affected dogs had a higher percentage of notched T waves than unaffected dogs (41.6% 5 10.8% vs 5.0% -t 1.2%, respectively). Notched T waves did not appear at all times of day, nor were they present in all leads. Conclusion: Vector quantization and first derivative analyses were feasible and effective methods for detecting T wave abnormalities associated with the development of ventricular arrhythmias. These methods ultimately may be useful for risk stratification of patients susceptible to ventricular T wave; vector quantization; cardiac arrhythmias; sudden death arrhythmias and sudden death.

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Electrocardiogram signals, particularly those taken in ambulatory settings, are often corrupted by biological and electrical background noise. While analog filters can often provide a solution to this problem, an alternative is to use a fast Finite Impulse Response (F1R)filtering system that can be easily implemented without a Digital Signal Processing (DSP) processor based dataacquisition system. The filters developed are formed by cascading several moving average filters, and a short corrector filter. An algorithm is described that determines the best linear-phase corrector filter for any given cutoff frequency, sampling frequency, set of tap weights, and lengths of the moving average filters. At observed sampling rates of up to 500 Hz, the filtering systems consisting of the corrector and moving average filters were shorter in length and had less passband ripple than 31-ters designed with the Parks-McClellan, Kaiser, and H a m m i n g methods.

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