Preprint Technical Note Version 1 This version is not peer-reviewed

Respiratory Rate Estimation Based on Spectrum Decomposition

Version 1 : Received: 28 March 2018 / Approved: 29 March 2018 / Online: 29 March 2018 (05:17:58 CEST)

How to cite: Lee, S.; Kim, S. Respiratory Rate Estimation Based on Spectrum Decomposition. Preprints 2018, 2018030243 (doi: 10.20944/preprints201803.0243.v1). Lee, S.; Kim, S. Respiratory Rate Estimation Based on Spectrum Decomposition. Preprints 2018, 2018030243 (doi: 10.20944/preprints201803.0243.v1).

Abstract

We propose an electrocardiogram (ECG) signal-based algorithm to estimate the respiratory rate is a significant informative indicator of physiological state of a patient. The consecutive ECG signals reflect the information about the respiration because inhalation and exhalation make transthoracic impedance vary. The proposed algorithm extracts the respiration-related signal by finding out the commonality between the frequency and amplitude features in the ECG pulse train. The respiration rate can be calculated from the principle components after the procedure of the singular spectrum analysis. We achieved 1.7569 breaths per min of root-mean-squared error and 1.7517 of standard deviation with a 32-seconds signal window of the Capnobase dataset, which gives notable improvement compared with the conventional Autoregressive model based estimation methods.

Subject Areas

respiratory sinus arrhythmia (RSA); R-peak amplitude (RPA); QRS amplitude

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