Tensor-based analysis of ECG changes prior to in-hospital cardiac arrest

This works presents an analysis in the changes in beat morphology prior to in-hospital cardiac arrest. We have used tensor decomposition methods to extract features from the ECG signal. After preprocessing and R peak detection, a tensor is constructed for each ECG signal by segmenting the signal in individual heartbeats and stacking them in a 3D manner. The result of the tensor decomposition are 3 factor vectors corresponding to each tensor dimension. The temporal vector, representing the standard heartbeat over all leads in the signal, is further processed to calculate 10 different features: 4 features characterizing global changes in beat morphology and 6 detailed features describing changes in timing and amplitude of the waveforms. We analyzed a dataset of 20 patients who experienced a cardiac arrest in the intensive care unit at the end of the recording. For each patient, a stable signal (in the beginning of the recording) and an unstable signal (near the cardiac arrest) were extracted and processed. Statistical analysis of the results in both time windows (e.g. stable and unstable) show significant changes in the values of 2 out of 4 global parameters and 4 out of 6 detailed parameters. The results indicate that the use of tensor-based methods can be a robust way to characterize ECG changes, and may be a useful tool in identifying patients at risk for cardiac arrest.