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Electronics 2017, 6(4), 94;

Correction of the Unobtrusive ECG Using System Identification

Chair for Medical Information Technology, RWTH Aachen University, Pauwelsstr. 20, 52074 Aachen, Germany
Author to whom correspondence should be addressed.
Received: 5 September 2017 / Revised: 22 October 2017 / Accepted: 27 October 2017 / Published: 7 November 2017
(This article belongs to the Special Issue Data Processing and Wearable Systems for Effective Human Monitoring)
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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. View Full-Text
Keywords: electrocardiogram (ECG); unobtrusive measurement; signal processing; system identification electrocardiogram (ECG); unobtrusive measurement; signal processing; system identification

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Boehm, A.; Yu, X.; Leonhardt, S.; Teichmann, D. Correction of the Unobtrusive ECG Using System Identification. Electronics 2017, 6, 94.

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