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Automatic detection of respiratory rate from electrocardiogram, respiration induced plethysmography and 3D acceleration signals

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Abstract

Respiratory monitoring is increasingly used in clinical and healthcare practices to diagnose chronic cardio-pulmonary functional diseases during various routine activities. Wearable medical devices have realized the possibilities of ubiquitous respiratory monitoring, however, relatively little attention is paid to accuracy and reliability. In previous study, a wearable respiration biofeedback system was designed. In this work, three kinds of signals were mixed to extract respiratory rate, i.e., respiration inductive plethysmography (RIP), 3D-acceleration and ECG. In-situ experiments with twelve subjects indicate that the method significantly improves the accuracy and reliability over a dynamic range of respiration rate. It is possible to derive respiration rate from three signals within mean absolute percentage error 4.37% of a reference gold standard. Similarly studies derive respiratory rate from single-lead ECG within mean absolute percentage error 17% of a reference gold standard.

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Authors and Affiliations

  1. Biomedical Engineering Program, Sun Yat-sen University, Guangzhou, 510006, China

    Guan-zheng Liu  (刘官正)

  2. Shenzhen Institutes of Advanced Technology, Shenzhen, 518055, China

    Dan Wu  (吴丹), Zhan-yong Mei  (梅占勇), Qing-song Zhu  (朱青松) & Lei Wang  (王磊)

  3. The Shenzhen Key Laboratory for Low-cost Healthcare, Shenzhen, 518055, China

    Lei Wang  (王磊)

Authors
  1. Guan-zheng Liu  (刘官正)
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  2. Dan Wu  (吴丹)
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  3. Zhan-yong Mei  (梅占勇)
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  4. Qing-song Zhu  (朱青松)
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  5. Lei Wang  (王磊)
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Corresponding author

Correspondence to Lei Wang  (王磊).

Additional information

Foundation item: Project(2012M510207) supported by the China Postdoctoral Science Foundation; Projects(60932001, 61072031) supported by the National Natural Science Foundation of China; Project(2012AA02A604) supported by the National High Technology Research and Development Program of China; Project (2013ZX03005013) supported by the Next Generation Communication Technology Major Project of National Science and Technology, China; Project supported by the “One-hundred Talent” and the “Low-cost Healthcare” Programs of Chinese Academy of Sciences

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Liu, Gz., Wu, D., Mei, Zy. et al. Automatic detection of respiratory rate from electrocardiogram, respiration induced plethysmography and 3D acceleration signals. J. Cent. South Univ. 20, 2423–2431 (2013). https://doi.org/10.1007/s11771-013-1752-z

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  • DOI: https://doi.org/10.1007/s11771-013-1752-z

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