A morphology-preserving algorithm for denoising of EMG-contaminated ECG signals

Goal: Clinical interpretation of an electrocardiogram (ECG) can be detrimentally affected by noise. Removal of the electromyographic (EMG) noise is particularly challenging due to its spectral overlap with the QRS complex. The existing EMG-denoising algorithms often distort signal morphology, thus obscuring diagnostically relevant information. Methods: Here, a new iterative regeneration method (IRM) for efficient EMG-noise suppression is proposed. The main hypothesis is that the temporary removal of the dominant ECG components enables extraction of the noise with the minimum alteration to the signal. The method is validated on SimEMG database of simultaneously recorded reference and noisy signals, MIT-BIH arrhythmia database and synthesized ECG signals, both with the noise from MIT Noise Stress Test Database. Results: IRM denoising and morphology-preserving performance is superior to the wavelet- and FIR-based benchmark methods. Conclusions : IRM is reliable, computationally non-intensive, fast and applicable to any number of ECG channels recorded by mobile or standard ECG devices

A novel mobile transtelephonic system with synthesized 12-lead ECG

The problem of synthesizing the standard 12-lead electrocardiogram (ECG) from the signals recorded using three special ECG leads is studied in detail. The implementation of that concept into the design of a new mobile ECG transtelephonic system is presented. The system has two separate units: a stationary diagnostic-calibration center and a mobile ECG device with integrated electrodes. The patient records by himself three special leads with the mobile ECG recorder and sends data via cellular phone to the personal computer in the diagnostic center where standard 12-lead ECG is numerically reconstructed on the base of the patient transformation matrix previously calculated into the calibration process. The experimental study shows high accuracy of the reconstructed ECG