Respiratory motion estimation from ECG using dipole position tracking

Respiration affects the electrocardiogram (ECG) through heart motion and changes in thoracic conductivity, leading to the development of ECG-derived respiration (EDR). Here, we propose a dipole-based EDR approach that tracks the heart’s beat-to-beat rigid-body motion from standard 12-lead ECG recordings and encodes each beat as a six-dimensional log-SE(3) descriptor. An unsupervised Rayleigh-quotient formulation extracts a respiratory surrogate by maximizing spectral energy in the respiratory band (0.1–0.4 Hz) without requiring a reference signal. A cohort of 80 participants was analyzed, including healthy volunteers ( n = 51 , 49% women) and patients with ischemic heart disease ( n = 29 , 21% women). Performance was evaluated against amplitude-based methods (QRS pk and QRS integral) and a VCG-based approach using Pearson correlation (CC) and breathing rate error (MAE). The proposed method achieved median CC = 0.87 and MAE = 0.7 BPM, comparable to VCG (CC = 0.85 , MAE = 0.89 BPM) and superior to amplitude-based methods (CC = 0.75 – 0.78 , MAE = 0.93 – 1.24 BPM; p < 0.05 ). Performance was significantly reduced for all methods in ischemic patients ( p < 0.0001 ), and a significant sex–pathology interaction was observed ( p < 0.01 ). However, the dipole method remained the most accurate. A dedicated 128-electrode pipeline revealed a dominant cranio-caudal cardiac displacement of 7.6 mm consistent with imaging literature, and a sequential activation of all six degrees of freedom across the respiratory cycle. This framework provides a robust, physiologically grounded approach for respiratory estimation from ECG, with potential applications in clinical monitoring and non-invasive characterization of cardio-thoracic dynamics.