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Register a new userAutomated Diagnosis for Cardiac Diseases Based on ECG Signals Image Processing and Artificial Intelligence Techniques
التشخيص الآلي لأمراض القلب بالاعتماد على معالجة صور إشارات ECG و تقنيات الذكاء الصنعي
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أدى دخول الحاسب إلى العديد من المجالات, كالمجال الطبي, إلى تطوير تقنيات جديدة أدت إلى ازدهار هذه المجالات, مما ساعد الأطباء في كشف و تشخيص الأمراض بدقة و مصداقية, حيث تؤدي خبرة الطبيب بالإضافة إلى دقة الحاسب للوصول إلى مصداقية تشخيص عالية كما تساهم بشكل كبير في نجاح الجراحات العلاجية و إنقاذ كثير من الأرواح . يهدف البحث إلى اقتراح طريقة جديدة لاكتشاف و تصنيف أمراض القلب في صور إشارات ECG و ذلك باستخدام نظام الاستدلال العصبي الضبابي المتكيف ANFIS. تم تطبيق الطريقة المقترحة على قاعدة بيانات لصور إشارات ECG تتكون من 147 صورة تصاحبت كل منها مع التقرير الطبي المرافق, حيث استخدمت التقارير الطبية للتحقق من صحة الاكتشاف و التصنيف و قد حققت هذه الطريقة دقة عالية وصلت حتى 97% في عملية الاكتشاف و التصنيف. تم بناء النظام المقترح باستخدام برنامج MATLAB و ذلك بالاعتماد على كل من مكتبات معالجة الصورة و الشبكات العصبية و المنطق الضبابي.
The entry of computer to many areas, such as medical field, led to develop new technique that has led to the prosperity of these areas, and helped doctors to detect and diagnose diseases accurately and credibility, where the experience of the doctor in addition to the accuracy of computer lead to access to the credibility of high patient and save human lives. A new approach for cardiac diseases detection and classification in ECG signals images is proposed using Adaptive Neuro Fuzzy Inference System ANFIS. The proposed approach is applied on database containing (147) ECG images, each of them accompanied with its medical report. The medical reports were used to validate the detection and classification. The proposed method achieved a relatively high accuracy (97%) in detection and classification processes. The proposed approach is developed using MATLAB, and based on its libraries, image processing, neural network and fuzzy logic.
References used
JANG,J. ANFIS: Adaptive – Network- Based – Fuzzy Inference System. California Univ, Berkeley, CA, USA. Vol 23, No.3, 2002, 665-685
OWEIS,R.J. ; SUNNA,M.J. A Combined Neuro–Fuzzy Approach for Classifying Image Pixels In Medical Applications. Journal of electrical engineering, VOL. 56, No. 5, 2005, 146–150
GULERA,I. ; UBEY,E.D. Ecg beat Classifier Designed By Combined Neural Network Model, Pattern Recognition Turkey, vol. 38, NO.2, 2005 , 199 – 208
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