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Journal of Research in Medical Sciences، جلد ۱۶، شماره ۲، صفحات ۰-۰
عنوان فارسی
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عنوان انگلیسی Detection and classification of cardiac ischemia using vectorcardiogram signal via neural network
چکیده انگلیسی مقاله 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. KEYWORDS: Vectorcardiography, Myocardial Ischemia, Neural Networks.
کلیدواژه‌های انگلیسی مقاله
نویسندگان مقاله علی رضا مهری دهنوی | ali reza mehri dehnavi
department of medical physics and engineering, school of medical, isfahan university of medical sciences, isfahan, iran

سازمان اصلی تایید شده: دانشگاه علوم پزشکی اصفهان (Isfahan university of medical sciences)

ایمان فرح آبادی | iman farahabadi
department of medical physics and engineering, school of medical, isfahan university of medical sciences, isfahan, iran

سازمان اصلی تایید شده: دانشگاه علوم پزشکی اصفهان (Isfahan university of medical sciences)

حسین ربانی | hossain rabbani
department of medical physics and engineering, school of medical, isfahan university of medical sciences, isfahan, iran

سازمان اصلی تایید شده: دانشگاه علوم پزشکی اصفهان (Isfahan university of medical sciences)

امین فرح آبادی | amin farahabadi
department of medical physics and engineering, school of medical, isfahan university of medical sciences, isfahan, iran

سازمان اصلی تایید شده: دانشگاه علوم پزشکی اصفهان (Isfahan university of medical sciences)

محمد پارسا محجوب | mohamad parsa mahjoob
school of medicine, jahrom university of medical sciences, jahrom, iran

سازمان اصلی تایید شده: دانشگاه علوم پزشکی جهرم (Jahrom university of medical sciences)

ناصر رجبی دهنوی | nasser rajabi dehnavi
saee hospital, isfahan university of medical sciences, isfahan, iran

سازمان اصلی تایید شده: دانشگاه علوم پزشکی اصفهان (Isfahan university of medical sciences)

نشانی اینترنتی http://jrms.mui.ac.ir/index.php/jrms/article/view/5452
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زبان مقاله منتشر شده en
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نوع مقاله منتشر شده Original Articles
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