Sparse index decomposition for source separation of impedance plethysmography signals

Impedance plethysmography (IPG) is a non-invasive technique used to track blood volume changes associated with cardiac and respiratory activities. However, separating both components is challenging due to their overlapping spectral content. This work introduces a sparse index decomposition (SID) algorithm for source separation as an alternative to conventional methods. The proposed approach operates in the frequency domain and exploits sparsity to separate cardiac and respiratory components using Fourier coefficients and harmonic band analysis. The methodology is evaluated using both simulated and experimental IPG signals acquired under apnea and eupnea conditions, and the obtained results are compared with source separation approaches such as empirical mode decomposition (EMD) and sparse reconstruction methods (SRM) in terms of signal-to-noise ratio (SNR) and normalized root-mean-square error (NRMSE). Results show that SID accurately separates physiological sources while preserving their spectral structure, achieving SNR improvements of up to 30 dB and NRMSE values of 0.01 in simulated signals and below 0.026 in experimental measurements. Additionally, the SID algorithm demonstrates robustness to noise and motion artifacts, highlighting its potential applicability to other biomedical signals requiring high spectral selectivity.