Name
Abstract | ||
|---|---|---|
Cardiac arrhythmia or irregular heartbeats are an important feature to assess the risk on sudden cardiac death and other cardiac disorders. Automatic classification of irregular heartbeats is therefore an important part of ECG analysis. We propose a tensor-based method for single-and multi-channel irregular heartbeat classification. The method tensorizes the ECG data matrix by segmenting each signal beat-by-beat and then stacking the result into a third-order tensor with dimensions channel x time x heartbeat. We use the multilinear singular value decomposition to model the obtained tensor. Next, we formulate the classification task as the computation of a Kronecker Product Equation. We apply our method on the INCART dataset, illustrating promising results. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/EMBC.2017.8036856 | 2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Field | DocType | Volume |
Tensor,Cardiac arrhythmia,Computer science,Theoretical computer science,Artificial intelligence,Multilinear map,Computation,Singular value decomposition,Computer vision,Heartbeat,Kronecker product,Pattern recognition,Communication channel | Conference | 2017 |
ISSN | Citations | PageRank |
1094-687X | 0 | 0.34 |
References | Authors | |
8 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martijn Bousse | 1 | 16 | 2.67 |
| Griet Goovaerts | 2 | 4 | 3.89 |
| Nico Vervliet | 3 | 22 | 6.33 |
| Otto Debals | 4 | 50 | 6.55 |
| Sabine Van Huffel | 5 | 1058 | 149.38 |
| Lieven De Lathauwer | 6 | 3002 | 226.72 |