Morphological classification of heartbeats using similarity features and a two-phase decision tree

Significant clinical information can be obtained from the analysis of the dominant beat morphology. In such respect, the identification of the dominant beats and their averaging can be very helpful, allowing clinicians to perform the measurement of amplitudes and intervals on a beat much cleaner from noise than a generic beat selected from the entire ECG recording. In this paper an algorithm for the morphological classification of heartbeats based on a two-phase decision tree is described. Similarity features extracted from every beat are used in the decision trees for the identification of different morphological classes for the beats of the ECG signal. The results, in terms of dominant beat discrimination, have been evaluated on all annotated beats of the MIT-BIH arrhythmia database with sensitivity = 99.05%, specificity = 93.94%, positive predictive value (PPV) = 99.32% and negative predictive value (NPV) = 91.69%.. Satisfactory results have been also obtained on all the detected beats of the same database using an already published QRS detector developed by the same authors and obtaining sensitivity = 98.71%, specificity = 93.81%, PPV = 99.30% and NPV = 89.11%.