Time Synchronization of Multimodal Physiological Signals through Alignment of Common Signal Types and Its Technical Considerations in Digital Health

• Type of Material: Article
• Authors: Ran Xiao, Emory University; Cheng Ding, Emory University; Xiao Hu, Emory University
• Language: English
• Date: 2022-05-01
• Publisher: MDPI
• Publication Version: Final Published Version
• Copyright Statement: © 2022 by the authors.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.3390/jimaging8050120
• Title of Journal or Parent Work: Journal of Imaging
• Volume: 8
• Issue: 5
• Grant/Funding Information: This research received no external funding.
• Keywords: time alignment; digital health; multimodal signals; cross-correlation
• Research Categories: Health Sciences, Nursing

Abstract

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.