Reliability estimation of armchair-based capacitive ECG using video-based pose estimation

Satyam Srivastava; Arman Ershadi; Mostafa Haghi; Thomas M. Deserno

doi:10.1117/12.2654327

10 April 2023 Reliability estimation of armchair-based capacitive ECG using video-based pose estimation

Satyam Srivastava, Arman Ershadi, Mostafa Haghi, Thomas M. Deserno

Proceedings Volume 12469, Medical Imaging 2023: Imaging Informatics for Healthcare, Research, and Applications; 124690E (2023) https://doi.org/10.1117/12.2654327
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Abstract

Background: Annually and globally, cardiovascular diseases yield the death of 17.9 million people. Continuous and unobtrusive monitoring of vital signs supports an early detection of abnormalities and diseases, such as atrial fibrillation. Here, we analyze capacitive electrocardiography (cECG) recorded in an armchair at home. However, processing such data is challenging, as body movements and other artifacts disturb the signal quality. Methods: In this paper, we suggest video-based pose estimation to assess the reliability of cEGC. In 20 subjects, we measured reference and capacitive ECG synchronized with a video recording for key-point-based movement analysis with the OpenPifPaf pose estimation algorithm. We considered all 17 human body joints to compute a movement index and label all data in windows of 5 s as reliable vs. unreliable, according to that index. Then, we compared the heart rates obtained from complete and reliable cEGC windows with the corresponding windows from the reference ground truth ECG. Result: The left and right hip joints are most significantly influencing the signal’s quality. In addition, the joints’ movement distance from the original position limited to the range 460.84 pixels and 382.22 pixels, respectively, deliver a reliable cECG signal. Conclusion: Video-based pose estimation delivers reliable and unreliable periods of cECG recordings and improves continuous health monitoring at home.

Conference Presentation

Citation Download Citation

Satyam Srivastava, Arman Ershadi, Mostafa Haghi, and Thomas M. Deserno "Reliability estimation of armchair-based capacitive ECG using video-based pose estimation", Proc. SPIE 12469, Medical Imaging 2023: Imaging Informatics for Healthcare, Research, and Applications, 124690E (10 April 2023); https://doi.org/10.1117/12.2654327

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