Radial basis function neural network approach for the diagnosis of coronary artery disease based on the standard electrocardiogram exercise test

Lewenstein, K.

doi:10.1007/bf02345292

Cited by 32 publications

(18 citation statements)

References 8 publications

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“…The RBFNN is a three-layer feed-forward network that generally uses a linear transfer function for the output units and a nonlinear transfer function (typically the Gaussian function) for the hidden units. In biomedical engineering, RBFNNs have been used in research such as Parkinson's disease prediction using EEG signals [35], diagnosis of coronary artery disease from ECG [17], and X-ray image intensifier [3]. In this study, we propose a novel method of reconstructing gastric slow wave from finger PPG signal using RBFNN.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of gastric slow wave from finger photoplethysmographic signal using radial basis function neural network

Yacin

Chakravarthy

Manivannan

2011

Med Biol Eng Comput

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Extraction of extra-cardiac information from photoplethysmography (PPG) signal is a challenging research problem with significant clinical applications. In this study, radial basis function neural network (RBFNN) is used to reconstruct the gastric myoelectric activity (GMA) slow wave from finger PPG signal. Finger PPG and GMA (measured using Electrogastrogram, EGG) signals were acquired simultaneously at the sampling rate of 100 Hz from ten healthy subjects. Discrete wavelet transform (DWT) was used to extract slow wave (0-0.1953 Hz) component from the finger PPG signal; this slow wave PPG was used to reconstruct EGG. A RBFNN is trained on signals obtained from six subjects in both fasting and postprandial conditions. The trained network is tested on data obtained from the remaining four subjects. In the earlier study, we have shown the presence of GMA information in finger PPG signal using DWT and cross-correlation method. In this study, we explicitly reconstruct gastric slow wave from finger PPG signal by the proposed RBFNN-based method. It was found that the network-reconstructed slow wave provided significantly higher (P < 0.0001) correlation (≥ 0.9) with the subject's EGG slow wave than the correlation obtained (≈0.7) between the PPG slow wave from DWT and the EEG slow wave. Our results showed that a simple finger PPG signal can be used to reconstruct gastric slow wave using RBFNN method.

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Section: Introductionmentioning

confidence: 99%

Reconstruction of gastric slow wave from finger photoplethysmographic signal using radial basis function neural network

Yacin

Chakravarthy

Manivannan

2011

Med Biol Eng Comput

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“…These networks have the ability to learn from experience, generalize from previous examples, and abstract relevant features from irrelevant data inputs [24]. Neural network applications in the domain of chronic disease management include automatic prediction of exacerbations in Chronic Obstructive Pulmonary Disorder [25]; diagnosing myocardial infarction [26,27,28,29], coronary artery disease [30,31,32], chronic heart failure [33]; predicting heart diseases [34]; classifying other types of heart disease [35]; diagnosing diabetes on small mobile devices [36]; and identifying behavioral health problems of patients who are at high risk for hospital admission [37]. In most chronic diseases, early detection is beneficial for effective management of the conditions.…”

Section: Research Backgroundmentioning

confidence: 99%

Preventive Healthcare: A Neural Network Analysis of Behavioral Habits and Chronic Diseases

Raghupathi

2017

Healthcare

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The research aims to explore the association between behavioral habits and chronic diseases, and to identify a portfolio of risk factors for preventive healthcare. The data is taken from the Behavioral Risk Factor Surveillance System (BRFSS) database of the Centers for Disease Control and Prevention, for the year 2012. Using SPSS Modeler, we deploy neural networks to identify strong positive and negative associations between certain chronic diseases and behavioral habits. The data for 475,687 records from BRFS database included behavioral habit variables of consumption of soda and fruits/vegetables, alcohol, smoking, weekly working hours, and exercise; chronic disease variables of heart attack, stroke, asthma, and diabetes; and demographic variables of marital status, income, and age. Our findings indicate that with chronic conditions, behavioral habits of physical activity and fruit and vegetable consumption are negatively associated; soda, alcohol, and smoking are positively associated; and income and age are positively associated. We contribute to individual and national preventive healthcare by offering a portfolio of significant behavioral risk factors that enable individuals to make lifestyle changes and governments to frame campaigns and policies countering chronic conditions and promoting public health.

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“…They have shown that RBFNN outperformed SVM for accurately classifying the tumors. Lewenstein [6] used RBFNN as a tool for diagnosis of coronary artery disease. The research was performed using 776 data records and over 90% accuracy was achieved for classifying.…”

Section: Related Workmentioning

confidence: 99%

A Clinical Decision Support System for Femoral Peripheral Arterial Disease Treatment

Yurtkuran

Tok

Emel

2013

Computational and Mathematical Methods in Medicine

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One of the major challenges of providing reliable healthcare services is to diagnose and treat diseases in an accurate and timely manner. Recently, many researchers have successfully used artificial neural networks as a diagnostic assessment tool. In this study, the validation of such an assessment tool has been developed for treatment of the femoral peripheral arterial disease using a radial basis function neural network (RBFNN). A data set for training the RBFNN has been prepared by analyzing records of patients who had been treated by the thoracic and cardiovascular surgery clinic of a university hospital. The data set includes 186 patient records having 16 characteristic features associated with a binary treatment decision, namely, being a medical or a surgical one. K-means clustering algorithm has been used to determine the parameters of radial basis functions and the number of hidden nodes of the RBFNN is determined experimentally. For performance evaluation, the proposed RBFNN was compared to three different multilayer perceptron models having Pareto optimal hidden layer combinations using various performance indicators. Results of comparison indicate that the RBFNN can be used as an effective assessment tool for femoral peripheral arterial disease treatment.

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Radial basis function neural network approach for the diagnosis of coronary artery disease based on the standard electrocardiogram exercise test

Cited by 32 publications

References 8 publications

Reconstruction of gastric slow wave from finger photoplethysmographic signal using radial basis function neural network

Reconstruction of gastric slow wave from finger photoplethysmographic signal using radial basis function neural network

Preventive Healthcare: A Neural Network Analysis of Behavioral Habits and Chronic Diseases

A Clinical Decision Support System for Femoral Peripheral Arterial Disease Treatment

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