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(a) Input signal frequencies exceed the Nyquist frequency are aliased.(b) With the frequency aliasing, P0.5 is two times of the real magnitude.
Source publication
In this paper we investigate frequency aliasing in spectral method of measuring T wave alter-nans, which may lead a high false positive rate. Microvolt T wave alternans(TWA) has been evaluated as a means of predicting occurrence of ventricular tachyarrhythmia events and its association with the genesis of ventricular ar-rhythmias has been demonstra...
Context in source publication
Context 1
... is, they are folded back or replicated at other positions in the spectrum above and below the Nyquist frequency. (See Figure 3) So in spectral method of measuring T wave alternans, power at the alternans frequency (P 0.5 ) which is used to indicate the level of alternation of T wave waveform is two times of the real magnitude of the original spectrum at 0.5 cycles/beat. ...
Citations
... where l and r are mean and standard deviation of spectral noise, P 0.5 is alternans power at 0.5 cycles/beats and (P 0.5 -l) is alternans voltage. The TWA is considered as significant if K score is more than three [53]. The HRT is a short-term fluctuation in sinus cycle length that follows spontaneous ventricular premature complex, in which consists of brief heart rate acceleration, followed by more gradual heart rate deceleration [54]. ...
Many studies showed electrocardiogram (ECG) parameters are useful for predicting fatal ventricular arrhythmias (VAs). However, the studies have several shortcomings. Firstly, all studies lack of effective way to present behavior of various ECG parameters prior to the occurrence of the VAs. Secondly, they also lack of discussion on how to consider the parameters as abnormal. Thirdly, the reports do not include approaches to increase the detection accuracy for the abnormal patterns. The purpose of this study is to address the aforementioned issues. It identifies ten ECG parameters from various sources and then presents a review based on the identified parameters. From the review, it has been found that the increased risk of VAs can be represented by presence and certain abnormal range of the parameters. The variation of parameters range could be influenced by either gender or age. This study also has discovered the facts that averaging, outliers elimination and morphology detection algorithms can contribute to the detection accuracy.
