Characterization of atrial fibrillation using the surface ECG: time-dependent spectral properties

Abstract

Time-frequency analysis is considered for characterizing atrial fibrillation in the surface electrocardiogram (ECG). Variations in fundamental frequency of the fibrillatory waves are tracked by using different time-frequency distributions which are appropriate to short- and long-term variations. The cross Wigner-Ville distribution is found to be particularly useful for short-term analysis due to its ability to handle poor signal-to-noise ratios. In patients with chronic atrial fibrillation, substantial short-term variations exist in fibrillation frequency and variations up to 2.5 Hz can be observed within a few seconds. Although time-frequency analysis is performed independently in each lead, short-term variations in fibrillation frequency often exhibit a similar pattern in the leads V1, V2 and V3. Using different techniques for short- and long-term analysis, it is possible to reliably detect subtle long-term changes in fibrillation frequency, e.g., related to an intervention, which otherwise would have been obscured by spontaneous variations in fibrillation frequency.

Time–frequency analysis of the surface electrocardiogram for monitoring antiarrhythmic drug effects in atrial fibrillation

“…[1][2][3] Briefly, after analog-to-digital conversion (2000 Hz, 12 bit, 0.05 -300 Hz) electrograms were stored on optical disk and transferred to a personal computer. After high-pass filtering to remove baseline wander, QRST complexes were subtracted using spatiotemporal QRST cancellation.…”

“…Higher harmonic frequency components are consequently associated with a smaller exponential decay and indicate more organized rhythms. [2,3] …”

ECG signal quantization effects in the analysis of atrial fibrillation

“…Recently, spectral analysis has been revealed as a useful tool for atrial fibrillation (AF) description [2]. Furthermore, time-domain methods have been successfully applied in the characterization of AF episodes using the surface electrocardiogram (ECG) [3,4]. The exhaustive analysis of AF episodes requires previously to separate the atrial activity (AA) component from other bioelectric signals that contribute to the ECG formation [5].…”

Applications of Signal Analysis to Atrial Fibrillation

“…Spectrogram of a one minute atrial activity signal computed with a 128 point FFT using a 2.5 seconds window length. Surface leads V1 to V3 are shown for comparison [39].…”

“…information is needed [39]. The DAF is known to be influenced by autonomic modulation and its variations over time have been studied in terms of the effects of parasympathetic and sympathetic stimulation as well as with respect to circadian rhythm.…”