scholarly journals Clinical Recognition of Sensory Ataxia and Cerebellar Ataxia

2021 ◽  
Vol 15 ◽  
Author(s):  
Qing Zhang ◽  
Xihui Zhou ◽  
Yajun Li ◽  
Xiaodong Yang ◽  
Qammer H. Abbasi
Keyword(s):  
Cerebellar Ataxia ◽  
Back Propagation ◽  
Principal Component ◽  
Economic Cost ◽  
The Body ◽  
Test Time ◽  
Support Vector ◽  
Learning Approaches ◽  
Sensory Ataxia ◽  
External Characteristics

Ataxia is a kind of external characteristics when the human body has poor coordination and balance disorder, it often indicates diseases in certain parts of the body. Many internal factors may causing ataxia; currently, observed external characteristics, combined with Doctor’s personal clinical experience play main roles in diagnosing ataxia. In this situation, different kinds of diseases may be confused, leading to the delay in treatment and recovery. Modern high precision medical instruments would provide better accuracy but the economic cost is a non-negligible factor. In this paper, novel non-contact sensing technique is used to detect and distinguish sensory ataxia and cerebellar ataxia. Firstly, Romberg’s test and gait analysis data are collected by the microwave sensing platform; then, after some preprocessing, some machine learning approaches have been applied to train the models. For Romberg’s test, time domain features are considered, the accuracy of all the three algorithms are higher than 96%; for gait detection, Principal Component Analysis (PCA) is used for dimensionality reduction, and the accuracies of Back Propagation (BP) neural Network, Support Vector Machine (SVM), and Random Forest (RF) are 97.8, 98.9, and 91.1%, respectively.

scholarly journals Identification of Pilots’ Fatigue Status Based on Electrocardiogram Signals

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3003
Author(s):  
Ting Pan ◽  
Haibo Wang ◽  
Haiqing Si ◽  
Yao Li ◽  
Lei Shang
Keyword(s):  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Practical Significance ◽  
Support Vector ◽  
State Identification ◽  
Feature Parameter ◽  
The Time Domain ◽  
Pilot Fatigue ◽  
Identification Model

Fatigue is an important factor affecting modern flight safety. It can easily lead to a decline in pilots’ operational ability, misjudgments, and flight illusions. Moreover, it can even trigger serious flight accidents. In this paper, a wearable wireless physiological device was used to obtain pilots’ electrocardiogram (ECG) data in a simulated flight experiment, and 1440 effective samples were determined. The Friedman test was adopted to select the characteristic indexes that reflect the fatigue state of the pilot from the time domain, frequency domain, and non-linear characteristics of the effective samples. Furthermore, the variation rules of the characteristic indexes were analyzed. Principal component analysis (PCA) was utilized to extract the features of the selected feature indexes, and the feature parameter set representing the fatigue state of the pilot was established. For the study on pilots’ fatigue state identification, the feature parameter set was used as the input of the learning vector quantization (LVQ) algorithm to train the pilots’ fatigue state identification model. Results show that the recognition accuracy of the LVQ model reached 81.94%, which is 12.84% and 9.02% higher than that of traditional back propagation neural network (BPNN) and support vector machine (SVM) model, respectively. The identification model based on the LVQ established in this paper is suitable for identifying pilots’ fatigue states. This is of great practical significance to reduce flight accidents caused by pilot fatigue, thus providing a theoretical foundation for pilot fatigue risk management and the development of intelligent aircraft autopilot systems.

scholarly journals Human body-fluid proteome: quantitative profiling and computational prediction

2020 ◽  
Author(s):  
Lan Huang ◽  
Dan Shao ◽  
Yan Wang ◽  
Xueteng Cui ◽  
Yufei Li ◽  
...  
Keyword(s):  
High Throughput ◽  
Human Body ◽  
Body Fluid ◽  
Biomarker Discovery ◽  
Interaction Network ◽  
Computational Prediction ◽  
Computational Effort ◽  
The Body ◽  
Support Vector ◽  
Learning Approaches

Abstract Empowered by the advancement of high-throughput bio technologies, recent research on body-fluid proteomes has led to the discoveries of numerous novel disease biomarkers and therapeutic drugs. In the meantime, a tremendous progress in disclosing the body-fluid proteomes was made, resulting in a collection of over 15 000 different proteins detected in major human body fluids. However, common challenges remain with current proteomics technologies about how to effectively handle the large variety of protein modifications in those fluids. To this end, computational effort utilizing statistical and machine-learning approaches has shown early successes in identifying biomarker proteins in specific human diseases. In this article, we first summarized the experimental progresses using a combination of conventional and high-throughput technologies, along with the major discoveries, and focused on current research status of 16 types of body-fluid proteins. Next, the emerging computational work on protein prediction based on support vector machine, ranking algorithm, and protein–protein interaction network were also surveyed, followed by algorithm and application discussion. At last, we discuss additional critical concerns about these topics and close the review by providing future perspectives especially toward the realization of clinical disease biomarker discovery.

ARRHYTHMIA DISEASE DIAGNOSIS USING NEURAL NETWORK, SVM, AND GENETIC ALGORITHM-OPTIMIZED k-MEANS CLUSTERING

2011 ◽  
Vol 11 (04) ◽  
pp. 897-915 ◽  
Author(s):  
ROSHAN JOY MARTIS ◽  
CHANDAN CHAKRABORTY
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Normal Sinus Rhythm ◽  
Optimization Technique ◽  
Back Propagation ◽  
Principal Component ◽  
Disease Diagnosis ◽  
Back Propagation Neural Network ◽  
Polynomial Kernel ◽  
Support Vector

This work aims at presenting a methodology for electrocardiogram (ECG)-based arrhythmia disease detection using genetic algorithm (GA)-optimized k-means clustering. The open-source ECG data from MIT-BIH arrhythmia database and MIT-BIH normal sinus rhythm database are subjected to a sequence of steps including segmentation using R-point detection, extraction of features using principal component analysis (PCA), and pattern classification. Here, the classical classifiers viz., k-means clustering, error back propagation neural network (EBPNN), and support vector machine (SVM) have been initially attempted and subsequently m-fold (m = 3) cross validation is used to reduce the bias during training of the classifier. The average classification accuracy is computed as the average over all the three folds. It is observed that EBPNN and SVM with different order polynomial kernel provide significant accuracies in comparison with k-means one. In fact, the parameters (centroids) of k-means algorithm are locally optimized by minimizing its objective function. In order to overcome this limitation, a global optimization technique viz., GA is suggested here and implemented to find more robust parameters of k-means clustering. Finally, it is shown that GA-optimized k-means algorithm enhances its accuracy to those of other classifiers. The results are discussed and compared. It is concluded that the GA-optimized k-means algorithm is an alternate approach for classification whose accuracy will be near to that of supervised (viz., EBPNN and SVM) classifiers.

scholarly journals Daily Natural Gas Load Forecasting Based on a Hybrid Deep Learning Model

Energies ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 218 ◽  
Author(s):  
Nan Wei ◽  
Changjun Li ◽  
Jiehao Duan ◽  
Jinyuan Liu ◽  
Fanhua Zeng
Keyword(s):  
Deep Learning ◽  
Natural Gas ◽  
Load Forecasting ◽  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Weather Factors ◽  
Original Dataset ◽  
Proposed Model

Forecasting daily natural gas load accurately is difficult because it is affected by various factors. A large number of redundant factors existing in the original dataset will increase computational complexity and decrease the accuracy of forecasting models. This study aims to provide accurate forecasting of natural gas load using a deep learning (DL)-based hybrid model, which combines principal component correlation analysis (PCCA) and (LSTM) network. PCCA is an improved principal component analysis (PCA) and is first proposed here in this paper. Considering the correlation between components in the eigenspace, PCCA can not only extract the components that affect natural gas load but also remove the redundant components. LSTM is a famous DL network, and it was used to predict daily natural gas load in our work. The proposed model was validated by using recent natural gas load data from Xi’an (China) and Athens (Greece). Additionally, 14 weather factors were introduced into the input dataset of the forecasting model. The results showed that PCCA–LSTM demonstrated better performance compared with LSTM, PCA–LSTM, back propagation neural network (BPNN), and support vector regression (SVR). The lowest mean absolute percentage errors of PCCA–LSTM were 3.22% and 7.29% for Xi’an and Athens, respectively. On these bases, the proposed model can be regarded as an accurate and robust model for daily natural gas load forecasting.

Arrhythmia Classification Based on Multiple Features Fusion and Random Forest Using ECG

2019 ◽  
Vol 9 (8) ◽  
pp. 1645-1654
Author(s):  
Zhizhong Wang ◽  
Hongyi Li ◽  
Chuang Han ◽  
Songwei Wang ◽  
Li Shi
Keyword(s):  
Random Forest ◽  
Wavelet Packet ◽  
Back Propagation ◽  
Principal Component ◽  
Support Vector ◽  
Features Fusion ◽  
Specificity And Sensitivity ◽  
Average Accuracy ◽  
Skewness Coefficient ◽  
Novel Method

Cardiovascular diseases have become more and more prominent in recent years, which have proven to be a major threat to people's health. Accurate detection of arrhythmia in patients has important implications for clinical treatment. The aim of this study was to propose a novel automatic classification method for arrhythmia in order to improve classification accuracy. The electrocardiogram (ECG) signal was subjected preprocessing for denoising purposes using a wavelet transform. Then, the local and global characteristics of the beat, which contained RR interval features according with the clinical diagnosis criterion, morphology features based on wavelet packet decomposition and statistical features along with kurtosis coefficient, skewness coefficient and variance are exploited and fused. Meanwhile, the dimensionality of wavelet packet coefficients were reduced via principal component analysis (PCA). Finally, these features were used as the input of the random forest classifier to train the model and were then compared with the support vector machine (SVM) and back propagation (BP) neural networks. Based on 100,647 beats from the MIT-BIH database, the proposed method achieved an average accuracy, specificity and sensitivity of 99.08%, 99.00% and 89.31%, respectively, using the intra-patient beats, and 92.31%, 89.98% and 37.47%, respectively, using the inter-patient beats. Moreover, two classification schemes, namely, inter-patient and intra-patient scheme, were validated. Compared with the other methods referred to in this paper, the performance of the novel method yielded better results.

Applied-Information Technology in Fusion SVM Partial Least Squares Analysis of Experimental Data in the Body of PET

2014 ◽  
Vol 951 ◽  
pp. 185-188
Author(s):  
Xian Zhou ◽  
Ying Xiong ◽  
Wang Ping Xiong ◽  
Ling Zhu Xiong
Keyword(s):  
Experimental Data ◽  
Least Squares ◽  
Partial Least Squares ◽  
Model Fitting ◽  
Principal Component ◽  
The Body ◽  
Support Vector ◽  
Linear Distribution ◽  
Partial Least Squares Analysis ◽  
Dependent Variables

Experimental data for the human body PET multi-variable, non-linear distribution and other characteristics, the use of fusion partial least squares support vector machine variables effectively extracted from the principal component, reducing the number of variables and the exclusion of noise information to construct a linear regression with the dependent variables model, fitting the model has good accuracy and generalization for PET clinical trials provide effective technical support and research ideas.

scholarly journals Rapid and Nondestructive On-Site Classification Method for Consumer-Grade Plastics Based on Portable NIR Spectrometer and Machine Learning

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yinglin Yang ◽  
Xin Zhang ◽  
Jianwei Yin ◽  
Xiangyang Yu
Keyword(s):  
Near Infrared ◽  
Nearest Neighbor ◽  
Diffuse Reflectance Spectrum ◽  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Support Vector ◽  
Site Classification ◽  
Classification Models ◽  
K Nearest Neighbor

The classification of plastic waste before recycling is of great significance to achieve effective recycling. In order to achieve rapid, nondestructive, and on-site detection, a portable near-infrared spectrometer was used in this study to obtain the diffuse reflectance spectrum for both standard and commercial plastics made by ABS, PC, PE, PET, PP, PS, and PVC. After applying a series of pretreatments, the principal component analysis (PCA) was used to analyze the cluster trend. K-nearest neighbor (KNN), support vector machine (SVM), and back propagation neural network (BPNN) classification models were developed and evaluated, respectively. The result showed that different plastics could be well separated in top three principal components space after pretreatment, and the classification models performed excellent classification results and high generalization capability. This study indicated that the portable NIR spectrometer, integrated with chemometrics, could achieve excellent performance and has great potential in the field of commercial plastic identification.

scholarly journals Improvement of ANN Models via Data Envelopment Analysis for Stock Prices Forecasting

2018 ◽  
Vol 7 (4.10) ◽  
pp. 282
Author(s):  
Raja Das ◽  
Jitendra Kumar Jaiswal ◽  
. .
Keyword(s):  
Data Envelopment Analysis ◽  
Hybrid Model ◽  
Stock Prices ◽  
Model Fitting ◽  
Principal Component ◽  
Support Vector ◽  
Data Envelopment ◽  
Learning Approaches ◽  
Ann Model ◽  
Ann Models

The implementation of artificial neural network techniques has become quite prevalent in the field of nonlinear data modelling and forecasting in this era. The only application of ANN models for model fitting may not be sufficient for close and satisfactory performances; hence the researchers are adopting hybrid models of ANN with different statistical and machine learning approaches such as support vector machines, particle sworm optimization, principal component analysis, etc. We have also developed a hybrid model in this paper with ANN and data envelopment analysis (DEA) techniques for stock prices forecasting in share market. The efficient decision making units have been selected with help of DEA approach and provided it as input to the Lavenberg-Marquardt technique based ANN model in sliding window manner. Further a closed performance of our hybrid model has been achieved by carrying out our experimentation with different number of nodes in the hidden layer of ANN model. Since the prices of stocks follow numerous factors such as demand and supply, political environments, economy and finance, buy and sell, etc., the historical prices for stocks may be convenient for the further forecasting.  

Study on Mid-Infrared Transmittance Spectroscopy for Fast Measurement of Crude Fat Content in Fish Feeds Based on BPNN and LS-SVM

2011 ◽  
Vol 460-461 ◽  
pp. 816-820 ◽  
Author(s):  
Feng Le Zhu ◽  
Yong He
Keyword(s):  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
Fat Content ◽  
Support Vector ◽  
Mid Infrared ◽  
Infrared Transmittance ◽  
Fish Feeds ◽  
Infrared Spectrometer ◽  
New Variables

The crude fat content in fish feeds was determined using mid-infrared transmittance spectroscopy and chemometrics fast and non-destructively. A total of 225 samples were prepared for spectra collecting from a FT/IR-4000 Fourier Transform Infrared Spectrometer (400-4000cm-1). Principal component analysis (PCA) was carried out and spectral data were compressed into several new variables, which can explain the most variance of original spectra. The first six PCs were used as inputs of back-propagation neural network (BPNN) and least squares-support vector machine (LS-SVM) to create the calibration models. Compared with BPNN, a slightly better prediction precision was achieved based on LS-SVM with correlation coefficient (R) = 0.9757 and root mean square error for prediction (RMSEP) = 0.2579. The overall results indicated that mid-infrared spectroscopy incorporated to chemometrics was promising for the accurate assessment of crude fat content in fish feeds.

GK BASED FUZZY CLUSTERING FOR THE DIAGNOSIS OF CARDIAC ARRHYTHMIA

2010 ◽  
Vol 09 (02) ◽  
pp. 105-123 ◽  
Author(s):  
AHMED M. MEHDI ◽  
ALADIN ZAYEGH ◽  
REZAUL BEGG ◽  
RUBBIYA ALI
Keyword(s):  
Cardiac Arrhythmia ◽  
Back Propagation ◽  
Single Layer ◽  
Principal Component ◽  
Receiver Operating Curve ◽  
Support Vector ◽  
Self Organizing Maps ◽  
Data Set ◽  
Feed Forward Back Propagation ◽  
The Right

Abstract-Cardiac arrhythmia is one of the major causes of human death, and most of the time it cannot be predicted well in advance at the right time. Computational intelligence algorithms can help in extracting the hidden patterns of biological datasets. This paper explores the use of advanced and intelligent computational algorithms for automated detection, classification and clustering of cardiac arrhythmia (CA). Application of Fuzzy C-Mean and Extended Fuzzy C-Mean method to the arrhythmia dataset (165 normal healthy and 138 with CA) demonstrated their good CA classification capabilities. Fuzzy C Mean algorithm was able to classify the two group of data set with an overall accuracy of 97.2% [sensitivity 96.4%, specificity 98.12% and area under the receiver operating curve (AUC-ROC = 0.963)]. The classification accuracy improved significantly when GK-based extended Fuzzy was employed, and an overall accuracy of 99.14% was achieved (sensitivity 97.11%, specificity 99.18% and AUC-ROC = 0.995). These accuracy results were respectively, 19.02%, 7%, 9.14% and 11.06% higher when compared to multi-input single layer perceptron (SLP), feed forward back propagation (FFBP), self organizing maps (SOM) and support vector machine (SVM). The performance measures of fuzzy techniques were found to be better if a Principal Component Analysis (PCA) technique was used to preprocess the arrhythmia datasets.

Sign in / Sign up

Export Citation Format

Share Document