A Wavelet-Based Algorithm for Delineation and Classification of Wave Patterns in Continuous Holter ECG Recordings.

Abstract

Quantitative analysis of the electrocardiogram (ECG) requires delineation and classification of the individual ECG wave patterns. We propose a wavelet-based waveform classifier that uses the fiducial points identified by a delineation algorithm. For validation of the algorithm, manually annotated ECG records from the QT database (Physionet) were used. ECG waveform classification accuracies were: 85.6% (P-wave), 89.7% (QRS complex), 92.8% (T-wave) and 76.9% (U-wave). The proposed classification method shows that it is possible to classify waveforms based on the points obtained during delineation. This approach can be used to automatically classify wave patterns in long-term ECG recordings such as 24-hour Holter recordings.