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the use of complex network similarity for the identification of atrial fibrillation.
The similarity of the network is estimated via the joint recurrence plot and
Hamming distance. Firstly, we transform multi-electrodes epicardium signals
recorded from dogs into the recurrence complex network. Then, we extract
features representing its similarity. Finally, epicardium signals are
classified utilizing the classification and regression tree with extracted features.
The method is validated using 1000 samples including 500 atrial fibrillation
cases and 500 normal sinus ones. The sensitivity, specificity and accuracy of
the identification are 98.2%, 98.8% and 98.5% respectively. This experiment
indicates that our approach may lay a foundation for the prediction of the
onset of atrial fibrillation.
Most existing reconstruction algorithms for photoacoustic
imaging (PAI) assume that transducers used to receive ultrasound signals
have infinite bandwidth. When transducers with finite bandwidth are used, this assumption
may result in reduction of the imaging contrast and distortions of reconstructed
images. In this paper, we propose a novel method to compensate the finite bandwidth
effect in PAI by using an optimal filter in the Fourier domain. Simulation results
demonstrate that the use of this method can improve the contrast of the reconstructed
images with finite-bandwidth ultrasound transducers.