A new approach to QRS segmentation based on wavelet bases and adaptive threshold technique

Abstract 

In this paper, we develop and evaluate a new approach to QRS segmentation based on the combination of two techniques: wavelet bases and adaptive threshold. Firstly, QRS complexes are identified without a preprocessing stage. Then, each QRS is segmented by identifying the complex onset and offset. We evaluated the algorithm on two manually annotated databases, the QT-database and the MIT-BIH Arrhythmia database. The QRS detector obtained a sensitivity of 99.02% and a positive predictivity of 99.35% over the first lead of the validation databases (more than 192,000 beats), while for the QT-database, values larger than 99.6% were attained. As for the delineation of the QRS complex, the mean and the standard deviation of the differences between the automatic and the manual annotations were computed. Using QT-database which contains recordings of annotated ECG with a sampling rate of 250Hz, we obtain the average of the differences not exceeding two sampling intervals, while the standard deviations were within acceptable range of values.

Keywords: Electrocardiogram (ECG), QRS complex, Wavelet transform (WT), Interval between beats (IBB), False positive (FP), False negative (FN)