Proposed algorithms for P-wave identification and segmentation usually search for it within a window just before the R peak, thus hypothesizing the presence of at most one P wave, as it is in a normal electrocardiographic (ECG) tracings. In presence of abnormal atrial depolarization, however, there might be no P waves (as in atrial fibrillation) or multiple P waves (as in second- or third-degree atrioventricular blocks). Thus, this study proposes a new Adaptive Threshold Identification Algorithm (AThrIA) for ECG P-waves whose most innovative feature is to look for P waves all along the heartbeat, potentially allowing multiple Pwaves identification. AThrIA ability to identify and segment (finding onset, maximum and offset) P waves was tested in simulated and experimental ECG tracings with no P waves, one P wave and two P waves, respectively. All P waves involved in the study were annotated. Results indicate that AThrIA correctly identified all P waves (no false-negative or false-positive detections). Segmentation errors were 0 ms for the simulated ECG tracings, and no more than 10 ms for the experimental tracings. Thus, AThrIA represents a promising tool for P-wave identification and segmentation in both physiological (one P wave) and pathological (none or multiple P waves) conditions.

AThrIA: A new adaptive threshold identification algorithm for electrocardiographic P Waves / Sbrollini, Agnese; Mercanti, Sofia; Agostinelli, Angela; Morettini, Micaela; Di Nardo, Francesco; Fioretti, Sandro; Burattini, Laura. - In: COMPUTING IN CARDIOLOGY. - ISSN 2325-8861. - ELETTRONICO. - 44:(2017), pp. 1-4. (Intervento presentato al convegno 44th Computing in Cardiology Conference, CinC 2017 tenutosi a Rennes, Francia nel 24-27/09/2017) [10.22489/CinC.2017.237-179].

AThrIA: A new adaptive threshold identification algorithm for electrocardiographic P Waves

Sbrollini, Agnese;Agostinelli, Angela;Morettini, Micaela;Di Nardo, Francesco;Fioretti, Sandro;Burattini, Laura
2017-01-01

Abstract

Proposed algorithms for P-wave identification and segmentation usually search for it within a window just before the R peak, thus hypothesizing the presence of at most one P wave, as it is in a normal electrocardiographic (ECG) tracings. In presence of abnormal atrial depolarization, however, there might be no P waves (as in atrial fibrillation) or multiple P waves (as in second- or third-degree atrioventricular blocks). Thus, this study proposes a new Adaptive Threshold Identification Algorithm (AThrIA) for ECG P-waves whose most innovative feature is to look for P waves all along the heartbeat, potentially allowing multiple Pwaves identification. AThrIA ability to identify and segment (finding onset, maximum and offset) P waves was tested in simulated and experimental ECG tracings with no P waves, one P wave and two P waves, respectively. All P waves involved in the study were annotated. Results indicate that AThrIA correctly identified all P waves (no false-negative or false-positive detections). Segmentation errors were 0 ms for the simulated ECG tracings, and no more than 10 ms for the experimental tracings. Thus, AThrIA represents a promising tool for P-wave identification and segmentation in both physiological (one P wave) and pathological (none or multiple P waves) conditions.
2017
978-1-5386-6630-2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/259393
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