Tensor-based detection of T wave alternans using ECG.

Abstract

T wave alternans is defined as changes in the T wave amplitude in an ABABAB-pattern. It can be found in ECG signals of patients with heart diseases and is a possible indicator to predict the risk on sudden cardiac death. Due to its low amplitude, robust automatic T wave alternans detection is a difficult task. We present a new method to detect T wave alternans in multichannel ECG signals. The use of tensors (multidimensional matrices) permits the combination of the information present in different channels, making detection more reliable. The possibility of decomposition of incomplete tensors is exploited to deal with noisy ECG segments. Using a sliding window of 128 heartbeats, a tensor is constructed of the T waves of all channels. Canonical Polyadic Decomposition is applied to this tensor and the resulting loading vectors are examined for information about the T wave behavior in three dimensions. T wave alternans is detected using a sign change counting method that is able to extract both the T wave alternans length and magnitude. When applying this novel method to a database of patients with multiple positive T wave alternans tests using the clinically available spectral method tests, both the length and the magnitude of the detected T wave alternans is larger for these subjects than for subjects in a control group.