Segmented beat modulation method for electrocardiogram estimation from noisy recordings

• A template-based filtering procedure reproducing heart-rate variability was proposed.
• Its robustness to noise, also in comparison to a standard template method was tested.
• A simulation study verifies the correctness of the proposed method implementation.
• A clinical study tested the method ability to estimate ECGs from noisy recordings.
• Clean ECGs were estimated from noisy recordings affected by heart rate variability.

Clinical utility of an electrocardiogram (ECG) affected by too high levels of noise such as baseline wanders, electrode motion artifacts, muscular artifacts and power-line interference may be jeopardized if not opportunely processed. Template-based techniques have been proposed for ECG estimation from noisy recordings, but usually they do not reproduce physiological ECG variability, which, however, provides clinically useful information on the patient's health. Thus, this study proposes the Segmented-Beat Modulation Method (SBMM) as a new template-based filtering procedure able to reproduce ECG variability, and assesses SBMM robustness to the aforementioned noises in comparison to a standard template method (STM). SBMM performs a unique ECG segmentation into QRS segment and TUP segment, and successively modulates/demodulates (by stretching or compressing) the former segments in order to adaptively adjust each estimated beat to its original morphology and duration. Consequently, SBMM estimates ECG with significantly lower estimation errors than STM when applied to recordings affected by various levels of the considered noises (SBMM: 176–232 µV and 79–499 µV; STM: 215–496 µV and 93–1056 µV, for QRS and TUP segments, respectively). Thus, SBMM is able to reproduce ECG variability and is more robust to noise than STM.