An Interpretable Distance Measure for Multivariate Non-Stationary Physiological Signals
Published in Proceedings of the Proceedings of the International Conference on Data Mining Workshops (ICDMW), 2023
Recommended citation: S. W. Combettes, C. Truong and L. Oudre, "An Interpretable Distance Measure for Multivariate Non-Stationary Physiological Signals," 2023 IEEE International Conference on Data Mining Workshops (ICDMW), Shanghai, China, 2023, pp. 533-539, doi: 10.1109/ICDMW60847.2023.00076. https://ieeexplore.ieee.org/abstract/document/10411636
Links: paper / PDF / code. Accepted at the ICDM 2023, AI4TS workshop.
Abstract:
We introduce $d_{symb}$, a novel distance measure for comparing multivariate non-stationary physiological signals. Unlike most distance measures on multivariate signals such as variants of Dynamic Time Warping (DTW), $d_{symb}$ can take into account their non-stationarity thanks to a symbolization step. This step is based on a change-point detection procedure, that splits a non-stationary signal into several stationary segments, followed by quantization using $K$-means clustering. The proposed distance measure leverages the general edit distance that is applied to the symbolic sequences. The performance of $d_{symb}$ compared to two commonly used DTW variants is illustrated by applying it to physiological signals recorded during walking protocols. In particular, $d_{symb}$ is shown to be interpretable: its symbolization detects the segments that correspond to salient behaviors. An open source GitHub repository is made available to reproduce all the experiments in Python.