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Journal Abstract Search

123 related items for PubMed ID: 9238371

  • 21. Neural networks for classification of ECG ST-T segments.
    Edenbrandt L, Devine B, Macfarlane PW.
    J Electrocardiol; 1992 Jul; 25(3):167-73. PubMed ID: 1645056
    [Abstract] [Full Text] [Related]

  • 22. Diagnostic criteria for computer-aided electrocardiographic 15-lead system. Evaluation using 12 leads and Frank orthogonal leads with vector display.
    Talbot S, Dreifus LS, Watanabe Y, Chiang R, Morris K, Reich M.
    Br Heart J; 1976 Dec; 38(12):1247-61. PubMed ID: 137730
    [Abstract] [Full Text] [Related]

  • 23. Are additional right precordial and left posterior ECG leads useful for the diagnosis of right ventricular infarct and posterior infarct? Also a plea for the revival of vectorcardiography.
    van Herpen G, Kors JA, Schijvenaars BJ.
    J Electrocardiol; 1999 Dec; 32 Suppl():51-4. PubMed ID: 10688302
    [No Abstract] [Full Text] [Related]

  • 24. Evaluation of a QRS scoring system for estimating myocardial infarct size. VIII. Specificity in a control group with left ventricular hypertrophy and proposal of a new scoring system for use with this confounding factor.
    Freye CJ, Wagner NB, Howe CM, Stack NC, Ideker RE, Selvester RH, Wagner GS.
    J Electrocardiol; 1992 Jan; 25(1):19-23. PubMed ID: 1531231
    [Abstract] [Full Text] [Related]

  • 25. Application of artificial neural networks for versatile preprocessing of electrocardiogram recordings.
    Mateo J, Rieta JJ.
    J Med Eng Technol; 2012 Feb; 36(2):90-101. PubMed ID: 22268996
    [Abstract] [Full Text] [Related]

  • 26. Artificial neural networks and ECG interpretation. Use and abuse.
    Dassen WR, Mulleneers RG, den Dulk K, Talmon JL.
    J Electrocardiol; 1993 Feb; 26 Suppl():61-5. PubMed ID: 8189149
    [Abstract] [Full Text] [Related]

  • 27. Artificial neural networks for the diagnosis of atrial fibrillation.
    Yang TF, Devine B, Macfarlane PW.
    Med Biol Eng Comput; 1994 Nov; 32(6):615-9. PubMed ID: 7723418
    [Abstract] [Full Text] [Related]

  • 28. Block-based neural networks for personalized ECG signal classification.
    Jiang W, Kong SG.
    IEEE Trans Neural Netw; 2007 Nov; 18(6):1750-61. PubMed ID: 18051190
    [Abstract] [Full Text] [Related]

  • 29. Bootstrap comparison of fuzzy ROC curves for ECG-LVH algorithms using data from the Framingham Heart Study.
    Campbell G, Levy D, Bailey JJ.
    J Electrocardiol; 1990 Nov; 23 Suppl():132-7. PubMed ID: 2151041
    [No Abstract] [Full Text] [Related]

  • 30. The Novacode criteria for classification of ECG abnormalities and their clinically significant progression and regression.
    Rautaharju PM, Park LP, Chaitman BR, Rautaharju F, Zhang ZM.
    J Electrocardiol; 1998 Jul; 31(3):157-87. PubMed ID: 9682893
    [Abstract] [Full Text] [Related]

  • 31. Methodology of serial ECG classification using an adaptation of the NOVACODE for Q wave myocardial infarction in the Bypass Angioplasty Revascularization Investigation (BARI).
    Chaitman BR, Zhou SH, Tamesis B, Rosen A, Terry AB, Zumbehl KM, Stocke K, Takase B, Gussak I, Rautaharju PM.
    J Electrocardiol; 1996 Oct; 29(4):265-77. PubMed ID: 8913901
    [Abstract] [Full Text] [Related]

  • 32. The added value of ECG-gating for the diagnosis of myocardial infarction using myocardial perfusion scintigraphy and artificial neural networks.
    Gjertsson P, Lomsky M, Richter J, Ohlsson M, Tout D, van Aswegen A, Underwood R, Edenbrandt L.
    Clin Physiol Funct Imaging; 2006 Sep; 26(5):301-4. PubMed ID: 16939508
    [Abstract] [Full Text] [Related]

  • 33. Discrimination of myocardial infarction stages by subjective feature extraction.
    Ge D, Sun L, Zhou J, Shao Y.
    Comput Methods Programs Biomed; 2009 Sep; 95(3):270-9. PubMed ID: 19394714
    [Abstract] [Full Text] [Related]

  • 34. A neuro-fuzzy approach to classification of ECG signals for ischemic heart disease diagnosis.
    Neagoe V-, Iatan I-, Grunwald S.
    AMIA Annu Symp Proc; 2003 Sep; 2003():494-8. PubMed ID: 14728222
    [Abstract] [Full Text] [Related]

  • 35. Body surface ECG potential maps in acute myocardial infarction.
    McMechan SR, MacKenzie G, Allen J, Wright GT, Dempsey GJ, Crawley M, Anderson J, Adgey AA.
    J Electrocardiol; 1995 Sep; 28 Suppl():184-90. PubMed ID: 8656109
    [Abstract] [Full Text] [Related]

  • 36. Combined wavelet transformation and radial basis neural networks for classifying life-threatening cardiac arrhythmias.
    al-Fahoum AS, Howitt I.
    Med Biol Eng Comput; 1999 Sep; 37(5):566-73. PubMed ID: 10723893
    [Abstract] [Full Text] [Related]

  • 37. Combination of diagnostic classifications from ECG and VCG computer interpretations.
    van Bemmel JH, Kors JA, van Herpen G.
    J Electrocardiol; 1992 Sep; 25 Suppl():126-30. PubMed ID: 1297678
    [Abstract] [Full Text] [Related]

  • 38. Echocardiography as an aid in understanding the electrocardiographic changes of left ventricular hypertrophy, ischemia, and infarction.
    Weiss JL.
    Ann N Y Acad Sci; 1990 Sep; 601():61-6. PubMed ID: 2145797
    [No Abstract] [Full Text] [Related]

  • 39. Cardiac arrhythmia classification using neural networks.
    Al-Nashash H.
    Technol Health Care; 2000 Sep; 8(6):363-72. PubMed ID: 11258582
    [Abstract] [Full Text] [Related]

  • 40. Multiscale Energy and Eigenspace Approach to Detection and Localization of Myocardial Infarction.
    Sharma LN, Tripathy RK, Dandapat S.
    IEEE Trans Biomed Eng; 2015 Jul; 62(7):1827-37. PubMed ID: 26087076
    [Abstract] [Full Text] [Related]

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