Automatic detection of T wave alternans using tensor decompositions in multilead ECG signals
In this study, a new method for the detection of T wave alternans in multichannel ECG signals is introduced. The use of tensors (multidimensional matrices) allows us to combine the information present in all channels, making detection more robust. To construct a 3D tensor from a 2D ECG signal, the T wave is first roughly segmented. The intervals are then placed after each other to obtain a 3D structure with dimensions time, space and heartbeats. The tensor is decomposed using Canonical Polyadic
... Canonical Polyadic Decomposition. The result is 1 rank-one tensor consisting of 3 loading vectors (which match the 3 dimensions of the original tensor). The third loading vector corresponds to the heartbeats dimension and gives information about the behavior of the T wave in different heartbeats. The Fourier transform of this loading vector can then be used to examine the presence of TWA. The methods have been tested on a subset of the T wave alternans database available on Physionet. Results show a very clear distinction between loading vectors of signals from both groups: the power of the loading vector in the TWA group is on average 100 times larger than in the control group. This suggests that tensors are an effective way of detecting TWA in multilead signals.