Key result
A graph-based image segmentation algorithm for remote vital sign estimation achieved an average Pearson correlation coefficient of 93.8% against gold standard contact-based ECG measurements.
Why the study?
Does a graph-based image segmentation algorithm improve the accuracy and robustness of remote vital sign estimation compared to conventional spectral magnitude methods?
Does a graph-based image segmentation algorithm improve the accuracy and robustness of remote vital sign estimation compared to conventional spectral magnitude methods?
Effect estimate: PCC 93.8%
A novel graph-based image segmentation algorithm applied to radar and video signals significantly improves the accuracy and robustness of contactless vital sign monitoring compared to conventional methods.
Supports remote monitoring tool refinement; leaves open clinical validation in patient populations.
Reliable and contactless measurements of vital signs, such as respiration and heart rate, are still unmet needs in clinical and home settings. Mm-wave radar and video-based technologies are promising, but currently, the signal processing-based vital sign extraction methods are prone to body motion disruptions or illumination variations in the surrounding environment. Here we propose an image segmentation-based method to extract vital signs from the recorded video and mm-wave radar signals. The proposed method analyses time-frequency spectrograms obtained from Short-Time Fourier Transform rather than individual time-domain signals. This leads to much-improved robustness and accuracy of the heart rate and respiration rate extraction over existing methods. The experiments were conducted under pre- and post-exercise conditions and were repeated on multiple individuals. The results are evaluated by using four metrics against the gold standard contact-based measurements. Significant improvements were observed in terms of precision, accuracy, and stability. The performance was reflected by achieving an averaged Pearson correlation coefficient (PCC) of 93.8% on multiple subjects. We believe that the proposed estimation method will help address the needs for the increasingly popular remote cardiovascular sensing and diagnosing posed by Covid-19.
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Yang et al. (2022) studied Healthy volunteers (vital sign monitoring) (n=3). Graph-based image segmentation algorithm on STFT spectrograms vs. Conventional spectral magnitude method was evaluated on Pearson correlation coefficient (PCC) against gold standard ECG (PCC 93.8%). A graph-based image segmentation algorithm for remote vital sign estimation achieved an average Pearson correlation coefficient of 93.8% against gold standard contact-based ECG measurements.
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