Key result
Cross-correlation time alignment cuts synchronization delay to ~0.1 seconds compared to DTW-based approaches.
Why the study?
Precise time synchronization of multimodal physiological signals from different devices remains challenging, as existing algorithms rely on specific physiological features that restrict their use cases.
Does a cross-correlation-based time alignment algorithm improve synchronization delay in multimodal physiological signals compared to a DTW-based approach?
Does a cross-correlation-based time alignment algorithm improve synchronization delay in multimodal physiological signals compared to a DTW-based approach?
Absolute Event Rate: 0.13% vs 1.44%
p-value: p=<0.001
A simple temporal cross-correlation algorithm can effectively synchronize timestamps of common physiological signals across different devices with a delay of 0.13 seconds.
May enable precise multimodal ECG integration in wearables; leaves open prospective clinical validation.
BACKGROUND: Despite advancements in digital health, it remains challenging to obtain precise time synchronization of multimodal physiological signals collected through different devices. Existing algorithms mainly rely on specific physiological features that restrict the use cases to certain signal types. The present study aims to complement previous algorithms and solve a niche time alignment problem when a common signal type is available across different devices. METHODS: We proposed a simple time alignment approach based on the direct cross-correlation of temporal amplitudes, making it agnostic and thus generalizable to different signal types. The approach was tested on a public electrocardiographic (ECG) dataset to simulate the synchronization of signals collected from an ECG watch and an ECG patch. The algorithm was evaluated considering key practical factors, including sample durations, signal quality index (SQI), resilience to noise, and varying sampling rates. RESULTS: The proposed approach requires a short sample duration (30 s) to operate, and demonstrates stable performance across varying sampling rates and resilience to common noise. The lowest synchronization delay achieved by the algorithm is 0.13 s with the integration of SQI thresholding. CONCLUSIONS: Our findings help improve the time alignment of multimodal signals in digital health and advance healthcare toward precise remote monitoring and disease prevention.
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Xiao et al. (2022) studied Suspicion of coronary artery diseases (n=32). Cross-correlation-based time alignment algorithm vs. Dynamic time warping (DTW)-based approach was evaluated on Synchronization delay (p=<0.001). The cross-correlation time alignment algorithm achieved a mean synchronization delay of 0.13 seconds compared to 1.44 seconds with a DTW-based approach (p<0.001).
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