Hybrid Neuro-Fractal Analysis of ECG Signals to Predict Ischemia

Ecg Signals

In this thesis, we propose and implement a new hybrid approach using fractal analysis, statistical analysis and neural network computation to build a model for prediction the number of ischemia occurrence based on ECG recordings. The main advantage of the proposed approach over similar earlier related works is that first useful parameters from fractal analysis of the signal are extracted to build a model that includes both clinical characteristics and signal attributes. Statistical analysis such as binary logistic regression and multivariate linear regression are then used to further explore the relation of parameters in order to obtain a more accurate model. We show that the results compare well with those of earlier work and clearly indicate that the augmentation of the above mentioned approaches improves the prediction accuracy.

Hybrid Neuro-Fractal Analysis of ECG Signals to Predict Ischemia

10.32920/ryerson.14668470 ◽

2021 ◽

Author(s):

Hedieh Montazeri

Keyword(s):

Neural Network ◽

Logistic Regression ◽

Statistical Analysis ◽

Linear Regression ◽

Fractal Analysis ◽

Prediction Accuracy ◽

Clinical Characteristics ◽

Hybrid Approach ◽

Ecg Signals

AB1329-HPR THE CURRENT STATUS AND ASSOCIATED FACTORS OF FATIGUE IN CHINESE PATIENTS WITH GOUT

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2020-eular.6338 ◽

2020 ◽

Vol 79 (Suppl 1) ◽

pp. 1953.3-1953

Author(s):

J. Guo ◽

W. Zhou ◽

M. He ◽

Z. Gu ◽

C. Dong

Keyword(s):

Multiple Sclerosis ◽

Logistic Regression ◽

Linear Regression ◽

Sleep Quality ◽

Psychological Status ◽

Functional Disorder ◽

Stepwise Linear Regression ◽

Chi Square Analysis ◽

Anxiety Depression

Background:Fatigue of chronic diseases has been paid more and more attention. but the status of fatigue in gout patients has not been reported all the world[1].Objectives:In the absence of previous studies, our study aims to investigate the fatigue status, explore the potential predictors of fatigue and the effects of fatigue on health-related quality of life (HRQoL) among Chinese gout patients.Methods:This cross-sectional study was conducted from the Affiliated Hospital of Nantong University. A series of questionnaires were applied: Fatigue Scale-14 (FS-14), the 10 cm visual analog scale (VAS), the Patient Health Questionnaire (PHQ-9), the Generalized Anxiety Disorder questionnaire (GAD-7), the Pittsburgh Sleep Quality Index (PSQI), Health Assessment Questionnaire(HAQ), the Short Form 36 health survey (SF-36). Laboratory examinations were taken to obtain some biochemical indicators. Independent samples t-test, Mann–Whitney U-test, Chi-square analysis, Pearson /Spearman correlation, Stepwise linear regression and binary logistic regression were used to analyze the data.Results:411 gout patients were included in this study. Among them, more than 50% patients reported physical fatigue in FS-14, severe disease, poor psychological status and reduced HRQoL were associated with fatigue. Multiple stepwise linear regression and binary logistic regression were applied and showed that pain, sleep quality, anxiety, depression and functional disorder were the potential predictors of fatigue. In addition, we found that the more severe the fatigue, the lower the patient’s HRQoL.Conclusion:Fatigue among gout patients is exceedingly common. The results of this study suggested that rheumatologists should pay closely attention to gout patients who suffer from serious fatigue, especially those with pain, poorer sleep quality, anxiety, depression and functional disorder.References:[1]Henry, A., Tourbah, A., Camus, G., Deschamps, R., Mailhan, L., Castex, C., Gout, O. & Montreuil, M. (2019) Anxiety and depression in patients with multiple sclerosis: The mediating effects of perceived social support, Multiple sclerosis and related disorders. 27, 46-51.Disclosure of Interests:None declared

TXT-tool 1.081-6.1 A Comparative Study of the Binary Logistic Regression (BLR) and Artificial Neural Network (ANN) Models for GIS-Based Spatial Predicting Landslides at a Regional Scale

Landslide Dynamics: ISDR-ICL Landslide Interactive Teaching Tools ◽

10.1007/978-3-319-57774-6_10 ◽

2017 ◽

pp. 139-151 ◽

Cited By ~ 13

Author(s):

Jie Dou ◽

Hiromitsu Yamagishi ◽

Zhongfan Zhu ◽

Ali P. Yunus ◽

Chi Wen Chen

Keyword(s):

Neural Network ◽

Logistic Regression ◽

Comparative Study ◽

Regional Scale ◽

Ann Models ◽

Artificial Neural ◽

Artificial Neural Network Ann

Application of Connectionist Approach in Classification of Nutritional Status Among Arsenic Affected people in Rural Areas in Bangladesh

Anwer Khan Modern Medical College Journal ◽

10.3329/akmmcj.v5i2.21128 ◽

2014 ◽

Vol 5 (2) ◽

pp. 23-29

Author(s):

A Rahman ◽

M Akter ◽

AK Majumder

Keyword(s):

Neural Network ◽

Body Mass Index ◽

Logistic Regression ◽

Nutritional Status ◽

Body Mass ◽

Clinical Data ◽

Predictive Models ◽

Rural Areas ◽

Connectionist Approach

Various methods can be applied to build predictive models for the clinical data with binary outcome variables. This research aims to explore and compare the process of constructing common predictive models. Models based on an artificial neural network (the connectionist approach) and binary logistic regressions were compared in their ability to classifying malnourished subjects and those with over-weighted participants in rural areas of Bangladesh. Subjects were classified according to the indicator of nutritional status measured by body mass index (BMI). This study also investigated the effects of different factors on the BMI level of a sample population of 460 adults of six villages in Bangladesh. Demographic, enthropometric and clinical data were collected based on a total of 460 participants aged over 30 years from six villages in Bangladesh that were identified as mainly dependent on wells contaminated with arsenic. Out of 460(140 male and 320 females) participants 186(40.44%) were identified as malnourished (BMI<18.5 gm), and the remainder 274(59.56%) were found as over-weighted (BMI>18.5 gm). Among other factors, arsenic exposures were found as significant risk factors for low body mass index (BMI) with a higher value of odds ratio. This study shows that, binary logistic regression correctly classified 72.85% of cases with malnourished in the training datasets, 76.08% in the testing datasets and 75.26% of all subjects. The sensitivities of the neural network architecture for the training and testing datasets and for all subjects were 84.28%, 84.78% and 81.72% respectively, indicate better performance than binary logistic regression model. DOI: http://dx.doi.org/10.3329/akmmcj.v5i2.21128 Anwer Khan Modern Medical College Journal Vol. 5, No. 2: July 2014, Pages 23-29

Hybrid prediction method of ECG signals based on VMD, PSR, and RBF neural network

10.21203/rs.3.rs-42859/v1 ◽

2020 ◽

Author(s):

Fuying Huang ◽

Tuanfa Qin ◽

Limei Wang ◽

Haibin Wan

Keyword(s):

Neural Network ◽

Phase Space ◽

Delay Time ◽

Prediction Accuracy ◽

Rbf Neural Network ◽

Prediction Method ◽

Prediction Methods ◽

Embedding Dimension ◽

Ecg Signals ◽

Ecg Data

Abstract Background: It is significant for doctors and body area networks (BANs) to predict ECG signals accurately. At present, the prediction accuracy of many existing ECG prediction methods is generally low. In order to improve the prediction accuracy of ECG signals in BANs, a hybrid prediction method of ECG signals is proposed in this paper. Methods: The proposed prediction method combines variational mode decomposition (VMD), phase space reconstruction (PSR), and a radial basis function (RBF) neural network. First, the embedding dimension and delay time of PSR are calculated according to the trained set of ECG data. Second, the ECG data are decomposed into several intrinsic mode functions (IMFs). Third, the phase space of each IMF is reconstructed according to the embedding dimension and the delay time. Fourth, an RBF neural network is established and each IMF is predicted by the network. Finally, the prediction results of all IMFs are added to realize the final prediction result. Results: To evaluate the prediction performance of the proposed method, simulation experiments are carried out on ECG data from the MIT-BIH Arrhythmia Database. The experimental results show that the prediction index RMSE (root mean square error) of the proposed method is only 10-3 magnitude and that of some traditional prediction methods is 10-2 magnitude.Conclusions: Compared with some traditional prediction methods, the proposed method improves the prediction accuracy of ECG signals obviously.

Chronic Stress and Cardiovascular Disease among Individuals Exposed to Lead: A Pilot Study

Diseases ◽

10.3390/diseases8010007 ◽

2020 ◽

Vol 8 (1) ◽

pp. 7 ◽

Cited By ~ 2

Author(s):

Emmanuel Obeng-Gyasi ◽

Barnabas Obeng-Gyasi

Keyword(s):

Cardiovascular Disease ◽

Logistic Regression ◽

Linear Regression ◽

Chronic Stress ◽

Regression Models ◽

Disease Risk ◽

Geometric Mean ◽

Cardiovascular Disease Risk ◽

Clinical Markers

Chronic stress and cardiovascular disease risk were explored in a predominately middle-aged adult population exposed to elevated lead levels in this cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES) from the period 2007-2010. Elevated lead exposure was defined using the epidemiological threshold of a blood lead level (BLL) > 5 μg/dL as defined by the U.S. Centers for Disease Control and Prevention (CDC). Allostatic load (AL), a measure of chronic stress, was operationalized using 10 clinical markers. The geometric mean values for clinical cardiovascular disease risk markers of interest (a) Gamma glutamyl-transferase (GGT) (a marker of oxidative stress), and (b) non-HDL cholesterol (non-HDL-c) (a marker of cardiovascular disease risk) were explored among lead-exposed and less lead-exposed individuals with differential chronic stress (AL) levels. Associations between AL and GGT/non-HDL-C were analyzed using linear regression models. The likelihood of increased clinical markers in lead-exposed individuals with high compared to low AL was explored using binary logistic regression models. In analyzing lead-exposed as compared to less lead-exposed populations, the geometric mean of the variables of interest showed significant elevations among lead-exposed individuals as compared to less lead-exposed individuals. Simple linear regression revealed that AL was positively associated with the variables of interest among the lead-exposed. In binary logistic regression among the lead-exposed, those with high AL, as compared to those with low AL, had significantly higher odds of having elevated non-HDL-C. This study submits that those exposed to lead with increasing AL may experience adverse cardiovascular health outcomes.

Improving prediction accuracy of river discharge time series using a Wavelet-NAR artificial neural network

Journal of Hydroinformatics ◽

10.2166/hydro.2012.143 ◽

2012 ◽

Vol 14 (4) ◽

pp. 974-991 ◽

Cited By ~ 16

Author(s):

Shouke Wei ◽

Depeng Zuo ◽

Jinxi Song

Keyword(s):

Neural Network ◽

Time Series ◽

Hybrid Model ◽

River Discharge ◽

Prediction Accuracy ◽

Hybrid Approach ◽

Series Data ◽

Discharge Time ◽

Artificial Neural

This study developed a wavelet transformation and nonlinear autoregressive (NAR) artificial neural network (ANN) hybrid modeling approach to improve the prediction accuracy of river discharge time series. Daubechies 5 discrete wavelet was employed to decompose the time series data into subseries with low and high frequency, and these subseries were then used instead of the original data series as the input vectors for the designed NAR network (NARN) with the Bayesian regularization (BR) optimization algorithm. The proposed hybrid approach was applied to make multi-step-ahead predictions of monthly river discharge series in the Weihe River in China. The prediction results of this hybrid model were compared with those of signal NARNs and the traditional Wavelet-Artificial Neural Network hybrid approach (WNN). The comparison results revealed that the proposed hybrid model could significantly increase the prediction accuracy and prediction period of the river discharge time series in the current case study.

Application of Artificial Neural Network and Genetic Algorithm to Healthcarewaste Prediction

Journal of Artificial Intelligence and Soft Computing Research ◽

10.2478/jaiscr-2014-0017 ◽

2013 ◽

Vol 3 (4) ◽

pp. 243-250 ◽

Cited By ~ 3

Author(s):

Samira Arabgol ◽

Hoo Sang Ko

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Linear Regression ◽

Prediction Accuracy ◽

Negative Impact ◽

Healthcare Waste ◽

Mean Squared Errors ◽

Artificial Neural ◽

Artificial Neural Network Ann

Abstract Prompt and proper management of healthcare waste is critical to minimize the negative impact on the environment. Improving the prediction accuracy of the healthcare waste generated in hospitals is essential and advantageous in effective waste management. This study aims at developing a model to predict the amount of healthcare waste. For this purpose, three models based on artificial neural network (ANN), multiple linear regression (MLR), and combination of ANN and genetic algorithm (ANN-GA) are applied to predict the waste of 50 hospitals in Iran. In order to improve the performance of ANN for prediction, GA is applied to find the optimal initial weights in the ANN. The performance of the three models is evaluated by mean squared errors. The obtained results have shown that GA has significant impact on optimizing initial weights and improving the performance of ANN.

Statistical and Machine Learning Methods for Software Fault Prediction Using CK Metric Suite: A Comparative Analysis

ISRN Software Engineering ◽

10.1155/2014/251083 ◽

2014 ◽

Vol 2014 ◽

pp. 1-15 ◽

Cited By ~ 10

Author(s):

Yeresime Suresh ◽

Lov Kumar ◽

Santanu Ku. Rath

Keyword(s):

Neural Network ◽

Machine Learning ◽

Logistic Regression ◽

Linear Regression ◽

Object Oriented ◽

Fault Prediction ◽

Learning Methods ◽

Software Fault Prediction ◽

Machine Learning Methods ◽

Software Fault

Experimental validation of software metrics in fault prediction for object-oriented methods using statistical and machine learning methods is necessary. By the process of validation the quality of software product in a software organization is ensured. Object-oriented metrics play a crucial role in predicting faults. This paper examines the application of linear regression, logistic regression, and artificial neural network methods for software fault prediction using Chidamber and Kemerer (CK) metrics. Here, fault is considered as dependent variable and CK metric suite as independent variables. Statistical methods such as linear regression, logistic regression, and machine learning methods such as neural network (and its different forms) are being applied for detecting faults associated with the classes. The comparison approach was applied for a case study, that is, Apache integration framework (AIF) version 1.6. The analysis highlights the significance of weighted method per class (WMC) metric for fault classification, and also the analysis shows that the hybrid approach of radial basis function network obtained better fault prediction rate when compared with other three neural network models.

Comparison of Predictive Models in Classification of Nutritional Status among Arsenic Affected People in Rural Areas in Bangladesh: An Artificial Neural Network (ANN) and Logistic Regression Approach

Journal of Shaheed Suhrawardy Medical College ◽

10.3329/jssmc.v6i2.31773 ◽

2017 ◽

Vol 6 (2) ◽

pp. 71-75

Author(s):

Azizur Rahman ◽

Mariam Akter ◽

Ajit Kumar Majumder ◽

Md Atiqul Islam ◽

AFM Arshedi Sattar

Keyword(s):

Neural Network ◽

Logistic Regression ◽

Clinical Data ◽

Predictive Models ◽

Rural Areas ◽

Binary Outcome ◽

Artificial Neural

Background: Clinical data play an important role in medical sector for binary outcome variables. Various methods can be applied to build predictive models for the clinical data with binary outcome variables.Objective: This research was aimed to explore and compare the process of constructing common predictive models.Methodology: Models based on an artificial neural network (the connectionist approach) and binary logistic regressions were compared in their ability to classifying malnourished subjects and those with over-weighted participants in rural areas of Bangladesh. Subjects were classified according to the indicator of nutritional status measured by body mass index (BMI). This study also investigated the effects of different factors on the BMI level of adults of six Villages in Bangladesh. Demographic, anthropometric and clinical data were collected based on aged over 30 years from six Villages in Bangladesh that were identified as mainly dependent on wells contaminated with arsenic.Result: A total of 460 participants were recruited for this study. Out of 460(140 male and 320 females) participants 186(40.44%) were identified as malnourished (BMK18.5 gm), and the remainder 274(59.56%) were found as over-weighted (BMI>18.5 gm). Among other factors, arsenic exposures were found as significant risk factors for low body mass index (BMI) with a higher value of odds ratio. This study shows that, binary logistic regression correctly classified 72.85% of cases with malnourished in the training datasets, 76.08% in the testing datasets and 75.26% of all subjects. The sensitivities of the neural network architecture for the training and testing datasets and for all subjects were 84.28%, 84.78% and 81 .72% respectively, indicate better performance than binary logistic regression model.Conclusion: This study demonstrates a significant performance of artificial neural network than the binary logistic regression models in classification of malnourished participants from over-weighted ones.J Shaheed Suhrawardy Med Coll, 2014; 6(2):71-75