ECG-derived respiration (EDR) is a low-cost and productive means for capturing respiratory activity. In particular, as the primary procedure in some cardiorespiratory-related studies, the quality of EDR is decisive for the performance of subsequent analyses.

In this paper, we proposed a novel EDR method based on the feature derived from the first moment (mean frequency) of the power spectrum (FMS). After obtaining the EDR signal from the feature, we introduced the Interacting Multiple Model (IMM) smoother to enhance the similarity of the EDR signal to the reference respiration. The assessment of the approach consisted of two steps: 1) the performance of extracted feature was verified against R-peak misalignment and noise. 2) the enhancement of IMM smoother to EDR waveforms was evaluated based on waveform correlation and respiratory rate estimation. All the assessments were conducted under the Fantasia database and Drivers database.

The FMS improved robustness against R peak offsets compared to most established feature-based EDR algorithms, but a slight 5% improvement of waveform correlation against RR interval-based feature under accurate R peaks. The IMM smoother performed similarly with the Kalman filter in the static database but realized the enhancement of some extent of the EDR waveform in the ambulatory database.

The proposed method investigated frequency domain mapping of ECG morphological changes caused by respiratory modulation and explained the EDR signal as a non-stationary time series, which provided a direction of better fitting the natural respiration process and enhancing the EDR waveform.