دانلود مقاله انگلیسی رایگان:مدل مرحله بندی خودکار نارسایی قلبی مبتنی بر یادگیری عمیق - 2019
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  • Automatic staging model of heart failure based on deep learning Automatic staging model of heart failure based on deep learning
    Automatic staging model of heart failure based on deep learning

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Automatic staging model of heart failure based on deep learning


    ترجمه فارسی عنوان مقاله:

    مدل مرحله بندی خودکار نارسایی قلبی مبتنی بر یادگیری عمیق


    منبع:

    Sciencedirect - Elsevier - Biomedical Signal Processing and Control, 52 (2019) 77-83: doi:10:1016/j:bspc:2019:03:009


    نویسنده:

    Dengao Li∗, Xuemei Li, Jumin Zhao, Xiaohong Bai


    چکیده انگلیسی:

    Heart failure (HF) is a disease that is harmful to human health. Recent advances in machine learningyielded new techniques to train deep neural networks, which resulted in highly successful applica-tions in many pattern recognition tasks such as object detection and speech recognition. To improve thediagnostic accuracy of HF staging, this study evaluates the performance of deep learning-based modelson combined features for its categorization. We proposed a novel deep convolutional neural network-Recurrent neural network (CNN-RNN) model for automatic staging of heart failure diseases in real-timeand dynamically. We employed the data segmentation and data augmentation pre-processing datasetto make the classification performance of the proposed architecture better. Specifically, this paper useconvolutional neural network (CNN) as a feature extractor instead of training the entire network toextract the characteristics of the electrocardiogram (ECG) signals and form a feature set. We combine theabove feature set with other clinical features, feed the combined features to RNN for classification, andfinally obtain 5 classification results. Experiments shows that the CNN-RNN model proposed in this paperachieved an accuracy of 97.6%, the sensitivity of 96.3%, specificity of 97.4% and proportion of 97.1% fortwo seconds of ECG segments. We obtained an accuracy, sensitivity, specificity and proportion of 96.2%,96.9%, 95.7%, and 94.3% respectively for five seconds of ECG duration. The model can be used as an aid tohelp clinicians confirm their diagnosis.
    Keywords:Heart failure | Staging model | Deep learning | Deep CNN-RNN model


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 7
    حجم فایل: 600 کیلوبایت

    قیمت: رایگان


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