Publication Details |
| Category | Text Publication |
| Reference Category | Journals |
| DOI | 10.23919/CinC53138.2021.9662908 |
| Title (Primary) | Arrhythmia classification of reduced-lead electrocardiograms by scattering-recurrent networks |
| Author | Warrick, P.A.; Lostanlen, V.; Eickenberg, M.; Homsi, M.N.; Rodríguez, A.C.; Andén, J. |
| Source Titel | Computing in Cardiology |
| Year | 2021 |
| Department | MOLSYB |
| Volume | 48 |
| Page From | 1 |
| Page To | 4 |
| Language | englisch |
| Topic | T9 Healthy Planet |
| Keywords | electrocardiography; scattering transform; phase harmonic correlation; canonical correlation analysis; convolutional neural networks; long short-term memory networks |
| Abstract | We describe an automatic classifier of arrythmias based on 12-lead and reduced-lead electrocardiograms. Our classifier composes the scattering transform (ST) and a long short-term memory (LSTM) network. It is trained on PhysioNet/Computing in Cardiology Challenge 2021 data. The ST captures short-term temporal ECG modulations while reducing its sampling rate to a few samples per typical heart beat. We pass the output of the ST to a depthwise-separable convolution layer which combines lead responses separately for each ST coefficient and then combines resulting values across ST coefficients. At a deeper level, 2 LSTM layers integrate local variations of the input over long time scales. We train in an end-to-end fashion as a multilabel classification problem with a normal and 25 arrhythmia classes. We used canonical correlation analysis (CCA) for transfer learning from 12-lead ST representations to reduced-lead ones. For 12-, 6-, 4-, 3- and 2-leads, team “BitScattered” Challenge metrics on the hidden validation set were 0.46, 0.44, 0.45, 0.46 and 0.43; and on the hidden test set were 0.10, 0.11, 0.10, 0.10 and 0.10, respectively, ranking 34 th on the hidden test set. |
| Warrick, P.A., Lostanlen, V., Eickenberg, M., Homsi, M.N., Rodríguez, A.C., Andén, J. (2021): Arrhythmia classification of reduced-lead electrocardiograms by scattering-recurrent networks Computing in Cardiology 48 , 1 - 4 10.23919/CinC53138.2021.9662908 |
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