. 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.
ECG, PQRST-complex, respiratory rate, signal processing
© Association for Information and Communication Technologies www.ict-act.org Developed by tdelev