title: Detection and classification of cardiac ischemia using vectorcardiogram signal via neural network.
abstract: BACKGROUND
  Various techniques are used in diagnosing cardiac diseases. The electrocardiogram is one of these tools in common use. In this study vectorcardiogram (VCG) signals are used as a tool for detection of cardiac ischemia.
  
  
  METHODS
  VCG signals used in this study were obtained form 60 patients suspected to have ischemia disease and 10 normal candidates. Verification of the ischemia had done by the cardiologist during strain test by the evaluation of electrocardiogram (ECG) records and patient's clinical history. The recorder device was Cardiax digital recorder system. The VCG signals were recorded in Frank lead configuration system.
  
  
  RESULTS
  Extracted ischemia VCG signals have been configured with 22 features. Feature dimensionalities were reduced by the use of Independent Components Analysis and Principal Component Analysis tools. Results obtained from strain test indicated that among 60 subjects, 50 had negative results and 10 had positive results. Ischemia detection of neural network using VCG parameters indicates 86% accuracy. Classification result on neural network using ECG ischemia detection parameters is 73% accurate. Accumulative evaluation including VCG analysis and strain test indicates 90% consistency.
  
  
  CONCLUSIONS
  Regarding the obtained results in this study, VCG has higher accuracy than ECG, so that in cases which ECG signal cannot provide certain diagnosis of existence or non-existence of ischemia, VCG signal can help in a wider range. We suggest the use of VCG as an auxiliary low cost tool in ischemia detection.
authors.affiliation: Department of Medical Physics and Engineering, School of Medical, Isfahan University of Medical Sciences, Isfahan, Iran.
authors.forename: Ali Reza Mehri
authors.initials: AR
authors.lastname: Dehnavi
authors.forename: Iman
authors.initials: I
authors.lastname: Farahabadi
authors.forename: Hossain
authors.initials: H
authors.lastname: Rabbani
authors.forename: Amin
authors.initials: A
authors.lastname: Farahabadi
authors.forename: Mohamad Parsa
authors.initials: MP
authors.lastname: Mahjoob
authors.forename: Nasser Rajabi
authors.initials: NR
authors.lastname: Dehnavi
references.citation: Clin Cardiol. 2000 Jan;23(1):4-13
references.pmid: 10680023
references.citation: J Electrocardiol. 2004;37 Suppl:91-7
references.pmid: 15534816
references.citation: Annu Int Conf IEEE Eng Med Biol Soc. 2008;2008:1056-9
references.pmid: 19162844
references.citation: J Am Coll Cardiol. 2000 Nov 15;36(6):1737-45
references.pmid: 11092639
references.citation: Heart Rhythm. 2004 Sep;1(3):317-25
references.pmid: 15851177
references.citation: Eur Heart J. 1997 Aug;18(8):1288-95
references.pmid: 9458421
references.citation: Circulation. 1981 May;63(5):1025-9
references.pmid: 7471360
references.citation: J Electrocardiol. 2006 Apr;39(2):169-76
references.pmid: 16580414
references.citation: Comp Biochem Physiol A Mol Integr Physiol. 2004 Mar;137(3):577-83
references.pmid: 15123194
references.citation: J Electrocardiol. 2009 May-Jun;42(3):267-73
references.pmid: 19159900
references.citation: J Electrocardiol. 2007 Oct;40(4):343.e1-11
references.pmid: 17027837
references.citation: J Electrocardiol. 2008 Nov-Dec;41(6):675-8
references.pmid: 18490026
references.citation: IEEE Eng Med Biol Mag. 1997 Jul-Aug;16(4):33-40
references.pmid: 9241518
references.citation: Cardiology. 2000;93(3):183-90
references.pmid: 10965090