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Goldberger, A.L., Amaral, L.A.N., Glass, L., Hausdorff, J.M., Ivanov, P.C.H., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.-K. and Stanley, H.E. (2000) PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation, 101, e215-e220.
http://circ.ahajournals.org/cgi/content/full/101/23/e215

has been cited by the following article:

• TITLE: Electrocardiogram Feature Extraction and Pattern Recognition Using a Novel Windowing Algorithm

  AUTHORS: Muhammad Umer, Bilal Ahmed Bhatti, Muhammad Hammad Tariq, Muhammad Zia-ul-Hassan, Muhammad Yaqub Khan, Tahir Zaidi

  KEYWORDS: Electrocardiogram, PLI, Windowing, Arrhythmia, Intervals, Cardiac, PQRST

  JOURNAL NAME: Advances in Bioscience and Biotechnology, Vol.5 No.11, October 17, 2014

  ABSTRACT: This paper presents a Novel Windowing Algorithm for Electrocardiogram Feature Extraction and Pattern Recognition. The work presented here deals with a simple and efficient way of detecting ECG features that are P, Q, R, S and T waves. Windowing method is used to select these waves. Windows are based on varying R-R intervals. It has been tested on ECG simulator data and also on different records of the MIT-BIH arrhythmia database, producing satisfactory results. ECG timing intervals are also required for monitoring the cardiac condition of patients. Hence after feature detections ECG timing intervals like the PR interval, QRS duration, the QT interval, the QT corrected interval and Vent Rate are efficiently calculated using proposed Formulae.