Robustness of Persistence Diagrams to Time-Delay for Seismocardiogram Signal Quality Assessment

Afra Nawar, Farhan N. Rahman, Onur Selim Kilic, Amit J. Shah, Omer T. Inan

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Keywords: Topological Data Analysis, Persistence Diagram, Dynamic Time Feature Matching, Seismocardiogram, Signal Quality, Electrocardiogram-Free, Time-Delay Invariance

TL;DR: We show that Topological Data Analysis (TDA) using persistence diagrams performs better than state-of-the-art seismocardiogram (SCG) signal quality indexing algorithms such as DTFM when assessing the quality of time-delayed SCG beats.

Abstract: Seismocardiography is a potent non-invasive cardiovascular monitoring technique whose widespread adoption is currently limited in ambulatory settings due to its susceptibility to corruption from environmental noise. In the absence of a clean concurrently collected electrocardiogram (ECG) signal as a heartbeat reference, template matching paired with windowing methods can serve as a useful method by which to assess SCG signal quality. However, windowing methods can introduce a time-shift in the segmentation of the SCG beats as compared to a template due to persistently adapting heart rate. In this study, we assess the performance of a state-of-the-art SCG signal quality assessment algorithm, dynamic time feature matching (DTFM), in ranking SCG beats by signal-to-noise ratio when introducing an artificial time-delay. We compare this performance against that of a novel methodology based on topological data analysis (TDA) using persistence diagrams. We found no significant difference ($p$>0.05) in ranking performance between topological data analysis (TDA) and dynamic time feature matching (DTFM) when SCG beats were segmented by true R-peak locations. However, we found that TDA significantly outperformed DTFM ($p$<0.001) when SCG beats were segmented 100, 200, or 300 ms earlier than the R-peak locations. These results suggest the potential promise of TDA-based methods for robust ECG-free SCG signal quality analysis. These advancements may facilitate the analysis of longitudinal SCG data taken in out-of-clinic settings in situations where ECG monitoring is not viable.

Track: 3. Signal processing, machine learning, deep learning, and decision-support algorithms for digital and computational health

NominateReviewer: Afra Nawar ([e-mail])

Submission Number: 153