دانلود مقاله انگلیسی رایگان:Probabilistic grammar-based neuroevolution for physiological signal classification of ventricular tachycardia - 2019
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  • Probabilistic grammar-based neuroevolution for physiological signal classification of ventricular tachycardia Probabilistic grammar-based neuroevolution for physiological signal classification of ventricular tachycardia
    Probabilistic grammar-based neuroevolution for physiological signal classification of ventricular tachycardia

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Probabilistic grammar-based neuroevolution for physiological signal classification of ventricular tachycardia


    ترجمه فارسی عنوان مقاله:

    Probabilistic grammar-based neuroevolution for physiological signal classification of ventricular tachycardia


    منبع:

    Sciencedirect - Elsevier - Expert Systems With Applications, 135 (2019) 237-248: doi:10:1016/j:eswa:2019:06:012


    نویسنده:

    Pak-Kan Wong a , ∗, Kwong-Sak Leung a , Man-Leung Wong b


    چکیده انگلیسی:

    Ventricular tachycardia is a rapid heart rhythm that begins in the lower chambers of the heart. When it happens continuously, this may result in life-threatening cardiac arrest. In this paper, we apply deep learning techniques to tackle the problem of the physiological signal classification of ventricular tachy- cardia, since deep learning techniques can attain outstanding performance in many medical applications. Nevertheless, human engineers are required to manually design deep neural networks to handle differ- ent tasks. This can be challenging because of many possible deep neural network structures. Therefore, a method, called ADAG-DNE, is presented to automatically design deep neural network structures using deep neuroevolution. Our approach defines a set of structures using probabilistic grammar and searches for best network structures using Probabilistic Model Building Genetic Programming. ADAG-DNE takes advantages of the probabilistic dependencies found among the structures of networks. When applying ADAG-DNE to the classification problem, our discovered model achieves better accuracy than AlexNet, ResNet, and seven non-neural network classifiers. It also uses about 2% of parameters of AlexNet, which means the inference can be made quickly. To summarize, our method evolves a deep neural network, which can be implemented in expert systems. The deep neural network achieves high accuracy. Moreover, it is simpler than existing deep neural networks. Thus, computational efficiency and diagnosis accuracy of the expert system can be improved.
    Keywords: Physiological signal classification | Heart disease | Neuroevolution | Probabilistic grammar | Genetic programming | Deep neural network


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 12
    حجم فایل: 931 کیلوبایت

    قیمت: رایگان


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