Respiratory Signal Extraction from ECG Signal

  • Kejun Dong
  • Li ZhaoEmail author
  • Chengyu LiuEmail author


The extraction of respiration from physiological signals such as the electrocardiogram (ECG) and photoplethysmogram (PPG) has been explored for a long time. The proposed methods are mainly based on filters and features. However, the performances among methods are hardly compared and summarized. In this chapter, we focus on the studies of the typical feature-based ECG-derived respirations (EDR). The review of each method is given. The experiment is processed with rest ECG data and reference respiratory data collected synchronously over 60 s. Three parameters, waveform correlation C1, interval correlation C2, and respiratory rate RR, are introduced to evaluate each method under conditions of good and poor signal qualities. The results indicate that the optimal method should be determined by applications. For parameter C1, trough envelope-based method provides the highest similarity with reference waveform (0.8426) when the signal quality is good. However, it is easily affected by the noise, decreasing to −0.3219. For parameter C2, ECG area mean-based method has the highest similarity with intervals of reference signal (0.8162). Likewise, it performs no better than QRS complex area-based method (0.7013) when the signal quality is poor. In general, signal quality has an effect on the results of these methods.


Derived respiration Electrocardiogram (ECG) Feature-based 


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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Information Science and EngineeringSoutheast UniversityNanjingP. R. China
  2. 2.The State Key Laboratory of Bioelectronics, School of Instrument Science and EngineeringSoutheast UniversityNanjingChina

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