Respiration rate and variability are indicators of health-condition changes. In chronic disease management, it is becoming increasingly desirable to use wearable devices in order to minimize invasiveness and maximize comfort. However, not all wearable devices integrate sensors for direct acquisition of respiratory (DAR) signal. In these cases, the breathing extraction can be done through indirect methods, typically from the electrocardiogram (ECG). The aim of the present study is to propose a single-ECG-lead procedure based on the Segmented-Beat Modulation Method (SBMM) as a suitable tool for ECG-derived respiratory (EDR) signal estimation and respiration frequency (RF) identification. Clinical data consisted of combined measurements of two-lead (I and II) ECG and DAR signals from 20 healthy subjects ('CEBS' database by Physionet). Each respiration-affected ECG lead was submitted to a specifically designed SBMMbased procedure for EDR estimation by ECG subtraction. RF from EDR and DAR were identified as the frequency at which the Fourier spectrum has a maximum in the 0.07-1.00 Hz frequency range. Results indicated that mean RF values over the population from EDR signals ($0.27 \pm 0.09$ Hz and $0.27 \pm 0.09$ Hz from leads I and II, respectively) were not significantly different from that from DAR ($0.28 \pm 0.09$ Hz). Moreover, differences in RF identification ($0.01 \pm 0.03$ Hz and $0.00 \pm 0.02$ Hz from leads I and II, respectively) were, on average not significantly different from 0. Thus, SBMM-based procedure is robust and accurate for EDR estimation and RF identification.