scholarly journals Wavelet Scattering Transform for ECG Beat Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Zhishuai Liu ◽  
Guihua Yao ◽  
Qing Zhang ◽  
Junpu Zhang ◽  
Xueying Zeng
Keyword(s):  
Neural Network ◽  
Time Window ◽  
Probabilistic Neural Network ◽  
Time Windows ◽  
Principal Component ◽  
Translation Invariant ◽  
Ecg Signals ◽  
Novel Approach ◽  
Scattering Transform ◽  
Reduction Methods

An electrocardiogram (ECG) records the electrical activity of the heart; it contains rich pathological information on cardiovascular diseases, such as arrhythmia. However, it is difficult to visually analyze ECG signals due to their complexity and nonlinearity. The wavelet scattering transform can generate translation-invariant and deformation-stable representations of ECG signals through cascades of wavelet convolutions with nonlinear modulus and averaging operators. We proposed a novel approach using wavelet scattering transform to automatically classify four categories of arrhythmia ECG heartbeats, namely, nonectopic (N), supraventricular ectopic (S), ventricular ectopic (V), and fusion (F) beats. In this study, the wavelet scattering transform extracted 8 time windows from each ECG heartbeat. Two dimensionality reduction methods, principal component analysis (PCA) and time window selection, were applied on the 8 time windows. These processed features were fed to the neural network (NN), probabilistic neural network (PNN), and k-nearest neighbour (KNN) classifiers for classification. The 4th time window in combination with KNN (k=4) has achieved the optimal performance with an averaged accuracy, positive predictive value, sensitivity, and specificity of 99.3%, 99.6%, 99.5%, and 98.8%, respectively, using tenfold cross-validation. Thus, our proposed model is capable of highly accurate arrhythmia classification and will provide assistance to physicians in ECG interpretation.

scholarly journals Principal component articial neural network calibration models for the simultaneous spectrophotometric estimation of mefenamic acid and paracetamol in tablets

2006 ◽  
Vol 71 (11) ◽  
pp. 1207-1218
Author(s):  
Dondeti Satyanarayana ◽  
Kamarajan Kannan ◽  
Rajappan Manavalan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mefenamic Acid ◽  
Principal Component Regression ◽  
Principal Component ◽  
Simultaneous Estimation ◽  
Uv Spectrophotometry ◽  
Novel Approach ◽  
Marquardt Algorithm ◽  
Calibration Models

Simultaneous estimation of all drug components in a multicomponent analgesic dosage form with artificial neural networks calibration models using UV spectrophotometry is reported as a simple alternative to using separate models for each component. A novel approach for calibration using a compound spectral dataset derived from three spectra of each component is described. The spectra of mefenamic acid and paracetamol were recorded as several concentrations within their linear range and used to compute a calibration mixture between the wavelengths 220 to 340 nm. Neural networks trained by a Levenberg-Marquardt algorithm were used for building and optimizing the calibration models using MATALAB? Neural Network Toolbox and were compared with the principal component regression model. The calibration models were thoroughly evaluated at several concentration levels using 104 spectra obtained for 52 synthetic binary mixtures prepared using orthogonal designs. The optimized model showed sufficient robustness even when the calibration sets were constructed from a different set of pure spectra of the components. The simultaneous prediction of both components by a single neural network with the suggested calibration approach was successful. The model could accurately estimate the drugs, with satisfactory precision and accuracy, in tablet dosage with no interference from excipients as indicated by the results of a recovery study.

Using Probabilistic Neural Network to Select a Medical Specialist Agent

2010 ◽  
pp. 164-177 ◽  
Author(s):  
Vijay Kumar Mago ◽  
M. Syamala Devi ◽  
Ajay Bhatia ◽  
Ravinder Mehta
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Treatment Plan ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Medical Specialist ◽  
Childhood Disease ◽  
Specialist Doctor ◽  
Novel Approach ◽  
Multi Agent

The authors aim to design the Multi-agent system, in which the software agents interact with each other to diagnose a disease and decide the treatment plan(s). In this chapter, the authors present a novel approach of applying Probabilistic Neural Network (PNN) to classify the childhood disease and their respective medical specialist. Normally this classification is performed by the pediatricians. The system that has been presented here, imitates the behavior of a pediatrician while selecting super specialist doctor. This decision making mechanism will be embedded in an agent called Intelligent Pediatric Agent. To design the PNN, a database consisting of 104 records has been gathered. It includes 17 different sign symptoms and based on their values, one of the five super specialists is selected. A Back propagation Neural Network (BPNN) has also been designed to compare the results produced by the PNN and it is found that PNN is more promising.

Security of E-Health Systems Using Face Recognition Based on Convolutional Neural Network

2020 ◽  
Vol 2 (2) ◽  
pp. 37-41
Author(s):  
Zhixian Chen ◽  
Jialin Tang ◽  
Xueyuan Gong ◽  
Qinglang Su
Keyword(s):  
Neural Network ◽  
Face Recognition ◽  
Convolutional Neural Network ◽  
Principal Component ◽  
Activation Function ◽  
Support Vector ◽  
Data Set ◽  
Novel Approach ◽  
Rectified Linear Unit ◽  
The Face

In order to improve the low accuracy of the face recognition methods in the case of e-health, this paper proposed a novel face recognition approach, which is based on convolutional neural network (CNN). In detail, through resolving the convolutional kernel, rectified linear unit (ReLU) activation function, dropout, and batch normalization, this novel approach reduces the number of parameters of the CNN model, improves the non-linearity of the CNN model, and alleviates overfitting of the CNN model. In these ways, the accuracy of face recognition is increased. In the experiments, the proposed approach is compared with principal component analysis (PCA) and support vector machine (SVM) on ORL, Cohn-Kanade, and extended Yale-B face recognition data set, and it proves that this approach is promising.

scholarly journals Research on Quality Anomaly Recognition Method Based on Optimized Probabilistic Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Li-li Li ◽  
Kun Chen ◽  
Jian-min Gao ◽  
Hui Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Control Chart ◽  
Control Charts ◽  
Probabilistic Neural Network ◽  
Principal Component ◽  
Original Data ◽  
Improved Genetic Algorithm ◽  
Training Time ◽  
Mixed Pattern

Aiming at the problems of the lack of abnormal instances and the lag of quality anomaly discovery in quality database, this paper proposed the method of recognizing quality anomaly from the quality control chart data by probabilistic neural network (PNN) optimized by improved genetic algorithm, which made up deficiencies of SPC control charts in practical application. Principal component analysis (PCA) reduced the dimension and extracted the feature of the original data of a control chart, which reduced the training time of PNN. PNN recognized successfully both single pattern and mixed pattern of control charts because of its simple network structure and excellent recognition effect. In order to eliminate the defect of experience value, the key parameter of PNN was optimized by the improved (SGA) single-target optimization genetic algorithm, which made PNN achieve a higher rate of recognition accuracy than PNN optimized by standard genetic algorithm. Finally, the above method was validated by a simulation experiment and proved to be the most effective method compared with traditional BP neural network, single PNN, PCA-PNN without parameters optimized, and SVM optimized by particle swarm optimization algorithm.

scholarly journals Effectiveness of Partition and Graph Theoretic Clustering Algorithms for Multiple Source Partial Discharge Pattern Classification Using Probabilistic Neural Network and Its Adaptive Version: A Critique Based on Experimental Studies

2012 ◽  
Vol 2012 ◽  
pp. 1-19
Author(s):  
S. Venkatesh ◽  
S. Gopal ◽  
K. Kannan
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Partial Discharge ◽  
Clustering Algorithms ◽  
Research Work ◽  
Experimental Studies ◽  
Multiple Sources ◽  
Graph Theoretic ◽  
Large Sets ◽  
Novel Approach

Partial discharge (PD) is a major cause of failure of power apparatus and hence its measurement and analysis have emerged as a vital field in assessing the condition of the insulation system. Several efforts have been undertaken by researchers to classify PD pulses utilizing artificial intelligence techniques. Recently, the focus has shifted to the identification of multiple sources of PD since it is often encountered in real-time measurements. Studies have indicated that classification of multi-source PD becomes difficult with the degree of overlap and that several techniques such as mixed Weibull functions, neural networks, and wavelet transformation have been attempted with limited success. Since digital PD acquisition systems record data for a substantial period, the database becomes large, posing considerable difficulties during classification. This research work aims firstly at analyzing aspects concerning classification capability during the discrimination of multisource PD patterns. Secondly, it attempts at extending the previous work of the authors in utilizing the novel approach of probabilistic neural network versions for classifying moderate sets of PD sources to that of large sets. The third focus is on comparing the ability of partition-based algorithms, namely, the labelled (learning vector quantization) and unlabelled (K-means) versions, with that of a novel hypergraph-based clustering method in providing parsimonious sets of centers during classification.

scholarly journals TRIGONOMETRIC SERIES IN ORTHOGONAL EXPANSIONS FOR DENSITY ESTIMATES OF DEEP IMAGE FEATURES

2018 ◽  
Vol 42 (1) ◽  
pp. 149-158 ◽  
Author(s):  
A. V. Savchenko
Keyword(s):  
Neural Network ◽  
Trigonometric Series ◽  
Probabilistic Neural Network ◽  
Principal Component ◽  
Image Features ◽  
Small Samples ◽  
Deep Convolutional Neural Networks ◽  
Orthogonal Expansions ◽  
Computational Speed ◽  
Reference Images

In this paper we study image recognition tasks in which the images are described by high dimensional feature vectors extracted with deep convolutional neural networks and principal component analysis. In particular, we focus on the problem of high computational complexity of a statistical approach with non-parametric estimates of probability density implemented by the probabilistic neural network. We propose a novel statistical classification method based on the density estimators with orthogonal expansions using trigonometric series. It is shown that this approach makes it possible to overcome the drawbacks of the probabilistic neural network caused by the memory-based approach of instance-based learning. Our experimental study with Caltech-101 and CASIA WebFace datasets demonstrates that the proposed approach reduces the error rate by 1–5 % and increases the computational speed by 1.5 – 6 times when compared to the original probabilistic neural network for small samples of reference images.

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