کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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877045 | 910881 | 2007 | 12 صفحه PDF | دانلود رایگان |
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 250 Hz, we obtain the average of the differences not exceeding two sampling intervals, while the standard deviations were within acceptable range of values.
Journal: Medical Engineering & Physics - Volume 29, Issue 1, January 2007, Pages 26–37