Cardiac Activation Mapping: The Amsterdam Years

2009 ◽  
pp. 1-9
Author(s):  
Hein J. Wellens
Keyword(s):  
2021 ◽  
Vol 77 (18) ◽  
pp. 429
Author(s):  
Swati Rao ◽  
Agatha Kwasnik ◽  
Hemal Nayak ◽  
Zaid Aziz ◽  
Gaurav Upadhyay ◽  
...  

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 371
Author(s):  
Yerin Lee ◽  
Soyoung Lim ◽  
Il-Youp Kwak

Acoustic scene classification (ASC) categorizes an audio file based on the environment in which it has been recorded. This has long been studied in the detection and classification of acoustic scenes and events (DCASE). This presents the solution to Task 1 of the DCASE 2020 challenge submitted by the Chung-Ang University team. Task 1 addressed two challenges that ASC faces in real-world applications. One is that the audio recorded using different recording devices should be classified in general, and the other is that the model used should have low-complexity. We proposed two models to overcome the aforementioned problems. First, a more general classification model was proposed by combining the harmonic-percussive source separation (HPSS) and deltas-deltadeltas features with four different models. Second, using the same feature, depthwise separable convolution was applied to the Convolutional layer to develop a low-complexity model. Moreover, using gradient-weight class activation mapping (Grad-CAM), we investigated what part of the feature our model sees and identifies. Our proposed system ranked 9th and 7th in the competition for these two subtasks, respectively.


2015 ◽  
Vol 31 (10) ◽  
pp. S245
Author(s):  
A. Porta-Sanchez ◽  
K. Viswanathan ◽  
N. Jackson ◽  
A. Al-Qubbany ◽  
M. Kusha ◽  
...  

Circulation ◽  
2015 ◽  
Vol 132 (suppl_3) ◽  
Author(s):  
Junaid A Zaman ◽  
Gautam G Lalani ◽  
Tina Baykaner ◽  
Shirley Park ◽  
David E Krummen ◽  
...  

Introduction: The mechanisms maintaining human persistent AF are elusive. It is striking how most optical mapping studies in animal and recently human AF show rotors and focal sources, while most classical activation mapping studies of electrograms do not. We tested the hypothesis that sites in human persistent AF showing rotors by phase analysis may, due to precession (‘wobble’) and fibrillatory collision, rarely reveal sources in activation maps. Methods: We studied 25 patients with persistent AF (LA 47 mm, CHADS2=1.9), in whom phase-mapping of electrograms from 64 pole baskets revealed rotors/focal sources where ablation terminated AF. Electrograms (fig A) were annotated (Matlab) using minimum dV/dt (unipoles, fig B) and peak amplitude criteria (bipoles) to create contours (isochrones), that were classified into a) complete, b) partial or c) unresolvable sources. Results: In each case, ablation at phase-identified rotors/sources (4.0±5.7 mins) terminated persistent AF to sinus rhythm (fig C, 64%) or atrial tachycardia. Notably, isochrones detected sources in only 5/25 (20%) of cases (fig D), more easily in unipolar than bipolar signals. Isochrones revealed partial sources in 11 (44%) and were unresolvable in 9 (36%). Source detection in classical maps was obscured by low signal: noise, varying sequence (rotor precession), or electrode noise that phase analysis resolved by analyzing neighboring sites (fig E). The figure summarizes these steps for a case with perfect agreement between activation and phase maps. Conclusions: Rotors and focal sources for human persistent AF detected by phase analysis were mostly undetected in activation maps, due to rotor precession and fibrillatory conduction. These data may inform approaches to revise classical criteria to better map AF.


2017 ◽  
Vol 46 (2) ◽  
pp. 257-269 ◽  
Author(s):  
Francisco Sahli Costabal ◽  
Junaid A. B. Zaman ◽  
Ellen Kuhl ◽  
Sanjiv M. Narayan

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