Correction of the Unobtrusive ECG Using System Identification

Abstract

Unobtrusively acquired electrocardiograms (ECG) could substantially improve the comfort of patients. However, such ECGs are not used in clinical practice because (among other reasons) signal deformations impede correct diagnosis of the ECG. Here, methods are proposed for correction of the unobtrusive ECG, based on system identification. Knowing the reference ECG, models were developed to correct the unobtrusively acquired ECG. A finite impulse response (FIR) model, a state space model and an autoregressive model were developed. The models were trained and evaluated on the Goldberger leads recorded from an ECG T-shirt with dry electrodes, and from a gold standard ECG. It was found that the FIR model corrects the unobtrusive ECG with good agreement (ρ aVR = 0.84 ± 0.10, ρ aVL = 0.65 ± 0.24, ρ aVF = 0.88 ± 0.04), while the other models do not yield significant improvements, or become unstable. The R-peaks were also accurately corrected by the FIR model (MSE aVR = 0.10 ± 0.10, MSE aVL = 0.14 ± 0.27, MSE aVF = 0.03 ± 0.02). To conclude, the proposed FIR method succeeded in significantly correcting the unobtrusive ECG signal.

Data Processing and Wearable Systems for Effective Human Monitoring

“…In the wide research area of electrocardiogram (ECG) processing, Boehm et al [2] developed a finite response model (FIR) to correct unobtrusively acquired ECG, which is commonly excluded from the clinical practice due to the signal deformations that can affect the ECG diagnosis. The model was compared with other approaches for the correction of ECG deformation showing significantly better results.…”