The proposed 32-bit integer wavelet-based ECG delineation algorithm achieved a QRS detection sensitivity of 99.77% and positive predictive value of 99.86% on the MIT-BIH Arrhythmia Database.
A novel 32-bit integer wavelet-based ECG delineation algorithm provides reliable QRS detection and accurate ECG delineation with reduced computational demands suitable for online processing.
BACKGROUND: Since the first well-known electrocardiogram (ECG) delineator based on Wavelet Transform (WT) presented by Li et al. in 1995, a significant research effort has been devoted to the exploitation of this promising method. Its ability to reliably delineate the major waveform components (mono- or bi-phasic P wave, QRS, and mono- or bi-phasic T wave) would make it a suitable candidate for efficient online processing of ambulatory ECG signals. Unfortunately, previous implementations of this method adopt non-linear operators such as root mean square (RMS) or floating point algebra, which are computationally demanding. METHODS: This paper presents a 32-bit integer, linear algebra advanced approach to online QRS detection and P-QRS-T waves delineation of a single lead ECG signal, based on WT. RESULTS: The QRS detector performance was validated on the MIT-BIH Arrhythmia Database (sensitivity Se = 99.77%, positive predictive value P+ = 99.86%, on 109010 annotated beats) and on the European ST-T Database (Se = 99.81%, P+ = 99.56%, on 788050 annotated beats). The ECG delineator was validated on the QT Database, showing a mean error between manual and automatic annotation below 1.5 samples for all fiducial points: P-onset, P-peak, P-offset, QRS-onset, QRS-offset, T-peak, T-offset, and a mean standard deviation comparable to other established methods. CONCLUSIONS: The proposed algorithm exhibits reliable QRS detection as well as accurate ECG delineation, in spite of a simple structure built on integer linear algebra.
Marco et al. (Sat,) conducted a other in Arrhythmia / ECG signal processing. 32-bit integer wavelet-based ECG delineation algorithm vs. Manual annotations was evaluated on QRS detection sensitivity on MIT-BIH Arrhythmia Database. The proposed 32-bit integer wavelet-based ECG delineation algorithm achieved a QRS detection sensitivity of 99.77% and positive predictive value of 99.86% on the MIT-BIH Arrhythmia Database.