Segmented beat modulation method for electrocardiogram estimation from noisy recordings

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 mu V and 79-499 mu V; STM: 215-496 mu V and 93-1056 mu V, for QRS and TUP segments, respectively). Thus, SBMM is able to reproduce ECG variability and is more robust to noise than STM.