Name
Abstract | ||
|---|---|---|
In participation of the PhysioNet/Computing in Cardiology Challenge 2020, we developed a novel computational approach for efficiently identifying cardiac abnormalities from 12-lead electrocardiogram (ECG) data. The developed methodology is composed of three processes: selecting representation, generating features, and predicting outcomes. We proposed a cache-inspired method to select a 12-lead ECG heartbeat representation. Moreover, we devised a physiologically interpretable feature generator for segmented 12-lead ECG signals. For multi-label arrhythmia classification, we innovated an efficient arrhythmia outcome prediction procedure that is adaptable to ECG data of variant lengths. Our team, JuJuRock, received a score of 0.402 using 5-fold cross-validation on the full training data and a score of 0.244 on the final full test data. Team JuJuRock ranked 16th out of the 41 teams that participated in this year's Challenge. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.22489/CinC.2020.134 | 2020 COMPUTING IN CARDIOLOGY |
DocType | ISSN | Citations |
Conference | 2325-8861 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Po-Ya Hsu | 1 | 5 | 3.47 |
| Po-Han Hsu | 2 | 1 | 1.69 |
| Tsung-Han Lee | 3 | 0 | 0.68 |
| Hsin-Li Liu | 4 | 0 | 0.34 |