Localized rotational activation in the left atrium during human atrial fibrillation: relationship to complex fractionated atrial electrograms and low-voltage zones

BACKGROUND

In humans, the existence of rotors or reentrant sources maintaining atrial fibrillation (AF) and the underlying electroanatomic substrate has not been well defined.

OBJECTIVE

Our aim was to determine the prevalence of localized rotational activation (RotA) in the left atrium (LA) during human AF and whether complex fractionated atrial electrograms (CFAEs) or low-voltage areas colocalize with RotA sites.

METHODS

We prospectively studied 32 patients (mean age 57 ± 8 years; 88% with persistent AF) undergoing AF catheter ablation. Bipolar electrograms were recorded for 2.5 seconds during AF using a roving 20-pole circular catheter in the LA. RotA was defined as sequential temporal activation of bipoles around the circular catheter. Bipolar electrogram fractionation index and bipolar voltage were used to define CFAEs and low-voltage areas, respectively.

RESULTS

In 21 (66%) patients, 47 RotA sites were identified. Few (9%) lasted 2.5 seconds (cycle length 183 ± 6 ms), while the majority (91%) were nonsustained (duration 610 ± 288 ms; cycle length 149 ± 11 ms). RotA was most common in the pulmonary vein antrum (71%) and posterior LA (25%). CFAEs were recorded from 18% ± 12% of LA area, and most (92% ± 7%) were not associated with RotA sites. However, 85% of RotA sites contained CFAEs. Very low voltage (<0.1 mV) areas comprised 12% ± 10% of LA area and were present in 23% of RotA sites.

CONCLUSIONS

In patients with predominantly persistent AF, localized RotA is commonly present but tends to be transient (<1 second). Although most CFAEs do not colocalize with RotA sites, the high prevalence of CFAEs and very low voltages within RotA sites may indicate slow conduction in diseased myocardium necessary for their maintenance.

Telephone-quality pathological speech classification using empirical mode decomposition

This paper presents a computationally simple and effective methodology based on empirical mode decomposition (EMD) for classification of telephone quality normal and pathological speech signals. EMD is used to decompose continuous normal and pathological speech signals into intrinsic mode functions, which are analyzed to extract physically meaningful and unique temporal and spectral features. Using continuous speech samples from a database of 51 normal and 161 pathological speakers, which has been modified to simulate telephone quality speech under different levels of noise, a linear classifier is used with the feature vector thus obtained to obtain a high classification accuracy, thereby demonstrating the effectiveness of the methodology. The classification accuracy reported in this paper (89.7% for signal-to-noise ratio 30 dB) is a significant improvement over previously reported results for the same task, and demonstrates the utility of our methodology for cost-effective remote voice pathology assessment over telephone channels.