Weighted Time Warping for Temporal Segmentation of Multi-Parameter Physiological Signals

Abstract

We present a novel approach to segmenting a quasiperiodic multi-parameter physiological signal in the presence of noise and transient corruption. We use Weighted Time Warping (WTW), to combine the partially correlated signals. We then use the relationship between the channels and the repetitive morphology of the time series to partition it into quasiperiodic units by matching it against a constantly evolving template. The method can accurately segment a multi-parameter signal, even when all the individual channels are so corrupted that they cannot be individually segmented. Experiments carried out on MIMIC, a multi-parameter physiological dataset recorded on ICU patients, demonstrate the effectiveness of the method. Our method performs as well as a widely used QRS detector on clean raw data, and outperforms it on corrupted data. Under additive noise at SNR 0 dB the average errors were 5:81 ms for our method and 303:48 ms for the QRS detector. Under transient corruption they were 2:89 ms and 387:32 ms respectively.