Technical Brief

Heart Signal Abnormality Detection Using Artificial Neural Networks1

[+] Author and Article Information
Ali Haider, Reza Fazel-Rezai

Department of Electrical Engineering,
University of North Dakota,
243 Centennial Drive, Stop 7165,
Grand Forks, ND 58202

Accepted and presented at the Design of Medical Devices Conference (DMD2014), Minneapolis, MN, April 7–10, 2014.

Manuscript received February 21, 2014; final manuscript received February 28, 2014; published online April 28, 2014. Editor: Arthur G. Erdman.

J. Med. Devices 8(2), 020915 (Apr 28, 2014) (2 pages) Paper No: MED-14-1044; doi: 10.1115/1.4027015 History: Received February 21, 2014; Revised February 28, 2014

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Christov, I., Jekova, I., and Bortolan, G., 2005, “Premature Ventricular Contraction Classification by the Kth Nearest-Neighbours Rule,” Physiol. Meas., 26(1), pp. 123–130. [CrossRef]
Afonso, V. X., and Tompkins, W. J., 1995, “Detecting Ventricular Fibrillation,” IEEE Eng. Med. Biol., 14(2), pp. 152–159. [CrossRef]
Watrous, R., and Towell, G., 1995, “A Patient-Adaptive Neural Network ECG Patient Monitoring Algorithm,” Comput. Cardiol, 10(13), pp. 229–232. [CrossRef]
Badr, A., 1993, “A Computer Environment for ECG Waveforms Morphological Classification,” Ph.D. thesis, Rijksuniversiteit Te Gent, Gent, Belgium.
Mark, R., and Moody, G., 1988, MIT-BIH Arrhythmia Database Directory, Massachusetts Institute of Technology, Cambridge, MA.


Grahic Jump Location
Fig. 1

ECG peaks as features




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