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Automated 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

3365   8   126   5.0 ( 1 )
 Publication date 2016
and research's language is العربية
 Created by Shamra Editor




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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.


Artificial intelligence review:
Research summary
يهدف هذا البحث إلى تطوير نظام تشخيص آلي لأمراض القلب باستخدام معالجة صور إشارات ECG وتقنيات الذكاء الاصطناعي، وبالتحديد نظام الاستدلال العصبي الضبابي المتكيف ANFIS. تم تطبيق النظام على قاعدة بيانات تتضمن 147 صورة ECG، حيث تم التحقق من صحة الاكتشاف والتصنيف باستخدام التقارير الطبية المرافقة. حقق النظام دقة عالية وصلت إلى 97% في عملية الاكتشاف والتصنيف. تم بناء النظام باستخدام برنامج MATLAB، معتمدين على مكتبات معالجة الصورة والشبكات العصبية والمنطق الضبابي. يتكون النظام من عدة مراحل تشمل المعالجة الأولية للصور، استخراج السمات، والتصنيف الآلي للأمراض. أظهرت النتائج كفاءة النظام في اكتشاف وتصنيف أمراض القلب بدقة عالية، مع توصيات لتحسين النظام من خلال تطبيق معالجة أولية مناسبة واستخلاص سمات إضافية.
Critical review
دراسة نقدية: بالرغم من أن البحث قدم نظاماً فعالاً لاكتشاف وتصنيف أمراض القلب باستخدام تقنيات الذكاء الاصطناعي، إلا أن هناك بعض النقاط التي يمكن تحسينها. أولاً، يمكن توسيع قاعدة البيانات المستخدمة لتشمل عينات أكثر تنوعاً من مختلف المستشفيات لضمان تعميم النتائج. ثانياً، يمكن تحسين مرحلة المعالجة الأولية للصور لتقليل نسبة الضجيج بشكل أكبر، مما قد يزيد من دقة التصنيف. ثالثاً، يمكن الاستفادة من تقنيات تعلم الآلة الأخرى مثل التعلم العميق لتحسين أداء النظام. وأخيراً، من المهم تشكيل فريق بحثي متعدد التخصصات يشمل أطباء ومهندسين لضمان دقة البيانات المستخدمة وتطبيقها بشكل صحيح.
Questions related to the research
  1. ما هو الهدف الرئيسي من البحث؟

    الهدف الرئيسي من البحث هو تطوير نظام تشخيص آلي لأمراض القلب باستخدام معالجة صور إشارات ECG وتقنيات الذكاء الاصطناعي، وبالتحديد نظام الاستدلال العصبي الضبابي المتكيف ANFIS.

  2. ما هي دقة النظام المقترح في اكتشاف وتصنيف أمراض القلب؟

    حقق النظام المقترح دقة عالية وصلت إلى 97% في عملية الاكتشاف والتصنيف.

  3. ما هي الأدوات البرمجية المستخدمة في بناء النظام؟

    تم بناء النظام باستخدام برنامج MATLAB، معتمدين على مكتبات معالجة الصورة والشبكات العصبية والمنطق الضبابي.

  4. ما هي التوصيات التي قدمها البحث لتحسين النظام؟

    التوصيات تشمل تحسين مرحلة اكتشاف المرض من خلال تطبيق معالجة أولية مناسبة على الصور، استخلاص سمات أخرى لأمراض القلب، وتشكيل فريق بحثي متعدد التخصصات يشمل أطباء ومهندسين.


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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