Estimation of Respiratory Rate from ECG signal in Python programming language
Date Issued
2022-09
Author(s)
Žňava, Eduard
Lehocki, Fedor
Tyšler, Milan
Madevska Bogdanova, Ana
Masár, Oto
Spasenović, Marko
Puteková, Silvia
Abstract
In case of mass casualties, it is necessary to obtain different vital signs
including respiratory rate effectively and accurately. The more physiological signals are measured individually - the more time it takes to obtain multiple vital
signs. In addition, a lot of technical equipment is needed. Because of that, it is
effective to derive multiple vital signs from measurement of one single physiological signal. It is possible to derive respiratory rate from ECG signal. In this
paper, we are constructing an appropriate solution based on different methods for
extraction of respiratory rate from ECG signal using Python programming language together with suitable Python libraries for data processing. We managed
to implement three methods and validate the accuracy of the calculations by Pearson’s and Spearman’s coefficients of correlation, as well as by root mean square
error between of the RR calculated from derived and measured respiration signal.
For the best method, we completed the algorithm reaching the coefficients of correlation equal to 0.703 and 0.700. The root mean square error is equal to 1.84
breaths per minute.
including respiratory rate effectively and accurately. The more physiological signals are measured individually - the more time it takes to obtain multiple vital
signs. In addition, a lot of technical equipment is needed. Because of that, it is
effective to derive multiple vital signs from measurement of one single physiological signal. It is possible to derive respiratory rate from ECG signal. In this
paper, we are constructing an appropriate solution based on different methods for
extraction of respiratory rate from ECG signal using Python programming language together with suitable Python libraries for data processing. We managed
to implement three methods and validate the accuracy of the calculations by Pearson’s and Spearman’s coefficients of correlation, as well as by root mean square
error between of the RR calculated from derived and measured respiration signal.
For the best method, we completed the algorithm reaching the coefficients of correlation equal to 0.703 and 0.700. The root mean square error is equal to 1.84
breaths per minute.
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