Non-invasive assessment of the atrial cycle length during atrial fibrillation in man: introducing, validating and illustrating a new ECG method.

OBJECTIVES: Atrial fibrillation (AF) in man has previously been shown to include a wide variety of atrial activity. Assessment of the characteristics of this arrhythmia with a commonly applicable tool may therefore be important in the choice and evaluation of different therapeutic strategies. As the AF cycle length has been shown to correlate locally with atrial refractoriness and globally with the degree of atrial organization, with, in general, shorter cycle length during apparently random AF compared to more organized AF, we have developed a new method for non-invasive assessment of the AF cycle length using the surface and the esophagus (ESO) ECG. METHODS AND RESULTS: From the frequency spectrum of the residual ECG, created by suppression of the QRST complexes, the dominant atrial cycle length (DACL) was derived. By comparison with multiple intracardiac simultaneously acquired right and left AF cycle lengths in patients with paroxysmal AF, we found that the DACL in lead V1, ranging from 130 to 185 ms, well represented a spatial average of the right AF cycle lengths, whereas the DACL in the ESO ECG, ranging from 140 to 185 ms, reflected both the right and the left AF cycle length, where the influence from each structure depended on the atrial anatomy of the individual, as determined by MRI. In patients with chronic AF, the method was capable of following changes in the AF cycle length due to administration of D,L-sotalol and 5 min of ECG recording was sufficient for the DACL to be reproducible. CONCLUSIONS: We conclude that this new non-invasive method, named 'Frequency Analysis of Fibrillatory ECG' (FAF-ECG), is capable of assessing both the magnitude and the dynamics of the atrial fibrillation cycle length in man.

Serial VCG/ECG analysis using neural networks.

Serial ECG analysis is an important diagnostic tool in which two or more successive ECG recordings from the same patient are compared in order to find changes due to, e.g. myocardial infarction. The present study investigates a new approach to serial analysis which is based on artificial neural networks. Interrecording changes are sometimes falsely detected due to electrode misplacement or positional changes of the heart. In order to compensate for such problems, a new technique for VCG loop alignment was employed. A study population of 1000 patients with two recordings was used and manually scrutinized by three experienced ECG interpreters. Pathological changes indicating newly developed infarcts were found in 256 patients. Different combinations of VCG/ECG measurements served as input data to the neural network. The best performance of the neural network was obtained when ECG and VCG measurements were combined and the resulting sensitivity was 69% at a specificity of 90%. The use of only ECG or VCG measurements reduced the sensitivity to 63% and 60%, respectively. The results indicated that serial analysis based on neural networks did not improve significantly when VCG loop alignment was included.