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
Sciencedirect - Elsevier - Biomedical Signal Processing and Control, 52 (2019) 77-83: doi:10:1016/j:bspc:2019:03:009
Dengao Li∗, Xuemei Li, Jumin Zhao, Xiaohong Bai
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