scholarly journals Classification of Electrocardiogram of Congenital Heart Disease Patients by Neural Network Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yongjie Yuan ◽  
Yongjun Zhang ◽  
Junyuan Wang ◽  
Ping Fang
Keyword(s):  
Neural Network ◽  
Congenital Heart Disease ◽  
Heart Disease ◽  
Congenital Heart ◽  
True Positive Rate ◽  
True Positive ◽  
Accuracy Rate ◽  
Network Algorithms ◽  
Positive Rate

The study intended to explore the effect of different neural network algorithms in the electrocardiogram (ECG) classification of patients with congenital heart disease (CHD). Based on the single convolutional neural network (CNN) ECG algorithm and the recurrent neural network (RNN) ECG algorithm, a multimodal neural network (MNN) ECG algorithm was constructed utilizing the MIT-BIH database as training set and test set. Furthermore, the MNN ECG algorithm was optimized to establish an improved MNN (IMNN) algorithm, which was applied to the diagnosis of CHD patients. The CHD patients admitted between August 2016 and August 2019 were selected for analysis to compare the classification effect and accuracy rate of IMNN, MNN, CNN ECG, and RNN ECG algorithms. It was found that the RNN ECG algorithm had higher classification sensitivity and true positive rate in terms of normal or bundle (NB) branch block beat, supraventricular abnormal (SA) rhythm, abnormal ventricular (AV) beat, and fusion beat (FB) than the CNN ECG algorithm ( P < 0.05 ), and the classification sensitivity and true positive rate of IMNN algorithm in the four aspects were significantly higher than those of MNN algorithm ( P < 0.05 ). The classification accuracy of CNN ECG algorithm and RNN ECG algorithm was above 98%, while that of MNN algorithm and IMNN algorithm was better than that of CNN ECG algorithm and RNN ECG algorithm, and the accuracy rate can reach 98.5% or more. Moreover, the accuracy rate of the IMNN algorithm can reach more than 98%. In conclusion, IMNN not only has a good classification ability in the simulated environment but also performs well in the actual environment, which is worthy of clinical promotion.

Angiographic classification of a single ventricle of the heart

1982 ◽  
Vol 63 (6) ◽  
pp. 44-46
Author(s):  
B. E. Shakhov
Keyword(s):  
Congenital Heart Disease ◽  
Cardiac Surgery ◽  
Heart Disease ◽  
Congenital Heart ◽  
Single Ventricle ◽  
Complex Congenital Heart Disease ◽  
Heart Ventricle ◽  
New Classification ◽  
Angiographic Examination

Based on the data of angiographic examination of 25 patients with a single heart ventricle, a new classification of complex congenital heart disease is proposed. It indicates the type of defect without taking into account the morphology of the single ventricular chamber, which is angiographically difficult or impossible to determine in some cases. This classification simplifies the interpretation of angiographic images, reflects the anatomical variants of the defect and meets the modern requirements of cardiac surgery.

Physiologic classification of congenital heart disease

AORN Journal ◽  
1973 ◽  
Vol 18 (1) ◽  
pp. 61-83 ◽  
Author(s):  
Lester R Sauvage ◽  
Peter B Mansfield ◽  
Stanley J Stamm
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Congenital Heart

scholarly journals Enhanced prediction of atrial fibrillation and mortality among patients with congenital heart disease using nationwide register based medical hospital data and neural networks

2021 ◽  
Author(s):  
Kok Wai Giang ◽  
Saga Helgadottir ◽  
Mikael Dellborg ◽  
Giovanni Volpe ◽  
Zacharias Mandalenakis
Keyword(s):  
Neural Network ◽  
Atrial Fibrillation ◽  
Neural Networks ◽  
Congenital Heart Disease ◽  
Logistic Regression ◽  
Heart Disease ◽  
Congenital Heart ◽  
Hospital Data ◽  
Neural Network Models ◽  
Over Time

Abstract Aims To improve short-and long-term predictions of mortality and atrial fibrillation among patients with congenital heart disease from a nationwide population using neural networks. Methods and results The Swedish National Patient Register and the Cause of Death Register were used to identify all patients with congenital heart disease born from 1970 to 2017. A total of 71,941 congenital heart disease patients were identified and followed-up from birth until the event or end of study in 2017. Based on data from a nationwide population, a neural network model was obtained to predict mortality and atrial fibrillation. Logistic regression based on the same data was used as a baseline comparison. Of 71,941 congenital heart disease patients, a total of 5768 died (8.02%) and 995 (1.38%) developed atrial fibrillation over time with a mean follow-up time of 16.47 years (standard deviation 12.73 years). The performance of neural network models in predicting the mortality and atrial fibrillation was higher than the performance of logistic regression regardless of the complexity of the disease, with an average Area Under the Receiver Operating Characteristic of &gt; 0.80 and &gt;0.70, respectively. The largest differences were observed in mortality and complexity of congenital heart disease over time. Conclusion We found that neural networks can be used to predict mortality and atrial fibrillation on a nationwide scale using data that are easily obtainable by clinicians. In addition, neural networks showed a high performance overall and, in most cases, with better performance for prediction as compared with more traditional regression methods.

Congenital Heart Disease-Corrleation of Pathologic Aanatomy and Angiocaridograpy, by Jesse E. Edwards, Lewis S. Carey, Henry R. Neufeld, Richard G. Lester. Philadelphia: W. B. Saunders, 1965, Volume I, pp. 1-396, Volume II, pp. 397-890, $45.00 the set

PEDIATRICS ◽  
1965 ◽  
Vol 36 (6) ◽  
pp. 965-965
Author(s):  
Sidney Friedman
Keyword(s):  
Congenital Heart Disease ◽  
Heart Disease ◽  
Congenital Heart ◽  
Pulmonary Vasculature ◽  
Pathologic Anatomy ◽  
The Status ◽  
Cardiovascular Anomalies ◽  
Accessible Form

These two profusely illustrated volumes containing over 2,500 illustrations attempt to present in a concise and readily accessible form a correlation of the pathologic anatomy of the heart with the roentgenographic and angiocardiographic findings in patients with congenital cardiovascular anomalies. In the organization of the text, a practical classification of cardiovascular anomalies is employed which is based upon two features: one, the presence or absence of cyanosis, and second, the status of the pulmonary vasculature as observed in the plain roentgenograms of the chest.

A hybrid optimization-based deep belief neural network for the classification of vegetation area in multi-spectral satellite image

2021 ◽  
Vol 24 (4) ◽  
pp. 363-379
Author(s):  
Anil B. Gavade ◽  
Vijay S. Rajpurohit
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Land Cover ◽  
False Positive Rate ◽  
Satellite Image ◽  
Harmony Search ◽  
Classification Problem ◽  
True Positive Rate ◽  
Positive Rate

Over the last few decades, multiple advances have been done for the classification of vegetation area through land cover, and land use. However, classification problem is one of the most complicated and contradicting problems that has received considerable attention. Therefore, to tackle this problem, this paper proposes a new Firefly-Harmony search based Deep Belief Neural Network method (FHS-DBN) for the classification of land cover, and land use. The segmentation process is done using Bayesian Fuzzy Clustering,and the feature matrix is developed. The feature matrix is given to the proposed FHS-DBN method that distinguishes the land coverfrom the land use in the multispectral satellite images, for analyzing the vegetation area. The proposed FHS-DBN method is designedby training the DBN using the FHS algorithm, which is developed by the combination of Firefly Algorithm (FA) and Harmony Search (HS) algorithm. The performance of the FHS-DBN model is evaluated using three metrics, such as Accuracy, True Positive Rate (TPR), and False Positive Rate (FPR). From the experimental analysis, it is concludedthat the proposed FHS-DBN model achieves ahigh classification accuracy of 0.9381, 0.9488, 0.9497, and 0.9477 usingIndian Pine, Salinas scene, Pavia Centre and university, and Pavia University scene dataset.

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