Literature DB >> 9450258

Detection of ECG waveforms by neural networks.

Z Dokur1, T Olmez, E Yazgan, O K Ersoy.   

Abstract

In this study, ECG waveform detection was performed by using artificial neural networks (ANNs). Initially, the R peak of the QRS complex is detected, and then feature vectors are formed by using the amplitudes of the significant frequency components of the DFT spectrum. Grow and Learn (GAL) and Kohonen networks are comparatively investigated to detect four different ECG waveforms. The comparative performance results of GAL and Kohonen networks are reported.

Mesh:

Year:  1997        PMID: 9450258     DOI: 10.1016/s1350-4533(97)00029-5

Source DB:  PubMed          Journal:  Med Eng Phys        ISSN: 1350-4533            Impact factor:   2.242


  5 in total

1.  R peak detection in electrocardiogram signal based on an optimal combination of wavelet transform, hilbert transform, and adaptive thresholding.

Authors:  Hossein Rabbani; M Parsa Mahjoob; E Farahabadi; A Farahabadi
Journal:  J Med Signals Sens       Date:  2011-05

2.  Revisiting QRS detection methodologies for portable, wearable, battery-operated, and wireless ECG systems.

Authors:  Mohamed Elgendi; Björn Eskofier; Socrates Dokos; Derek Abbott
Journal:  PLoS One       Date:  2014-01-07       Impact factor: 3.240

3.  Electrocardiogram Delineation in a Wistar Rat Experimental Model.

Authors:  Pedro David Arini; Sergio Liberczuk; Javier Gustavo Mendieta; Martín Santa María; Guillermo Claudio Bertrán
Journal:  Comput Math Methods Med       Date:  2018-02-08       Impact factor: 2.238

4.  Heart detection and diagnosis based on ECG and EPCG relationships.

Authors:  W Phanphaisarn; A Roeksabutr; P Wardkein; J Koseeyaporn; Pp Yupapin
Journal:  Med Devices (Auckl)       Date:  2011-08-26

5.  Classification of ECG signals using multi-cumulants based evolutionary hybrid classifier.

Authors:  Sahil Dalal; Virendra P Vishwakarma
Journal:  Sci Rep       Date:  2021-07-23       Impact factor: 4.379

  5 in total

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