Non-Invasive Approach to Predict the Cholesterol Level in Blood Using Bioimpedance and Neural Network Techniques

Mohktar, Mas S.; Ibrahim, Fatimah; Ismail, Noor Azina

doi:10.4015/s1016237213500464

Cited by 6 publications

(2 citation statements)

References 30 publications

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“…The best result is obtained with BPANN algorithm. Backpropagation artificial neural network (BPANN) is a type of artificial neural network that assumes the function of a common and complex nervous system, and BPANN is widely used in machine learning for clinical research [105,106]. BPANN is trained using Levenberg-Marquardt backpropagation algorithm [107].…”

Section: Nonlinear Features To Complexity Estimation For Building Mapmentioning

confidence: 99%

Complexity of Atrial Fibrillation Electrograms Through Nonlinear Signal Analysis: In Silico Approach

Tobón¹,

Duque²,

Ugarte³

et al. 2017

Interpreting Cardiac Electrograms - From Skin to Endocardium

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Identification of atrial fibrillation (AF) mechanisms could improve the rate of ablation success. However, the incomplete understanding of those mechanisms makes difficult the decision of targeting sites for ablation. This work is focused on the importance of EGM analysis for detecting and modulating rotors to guide ablation procedures and improve its outcomes. Virtual atrial models are used to show how nonlinear measures can be used to generate electroanatomical maps to detect critical sites in AF. A description of the atrial cell mathematical models, and the procedure of coupling them within two-dimensional and three-dimensional virtual atrial models in order to simulate arrhythmogenic mechanisms, is given. Mathematical modeling of unipolar and bipolar electrogramas (EGM) is introduced. It follows a discussion of EGM signal processing. Nonlinear descriptors, such as approximate entropy and multifractal analysis, are used to study the dynamical behavior of EGM signals, which are not well described by a linear law. Our results evince that nonlinear analysis of EGM can provide information about the dynamics of rotors and other mechanisms of AF. Furthermore, these fibrillatory patterns can be simulated using virtual models. The combination of features using machine learning tools can be used for identifying arrhythmogenic sources of AF.

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Section: Nonlinear Features To Complexity Estimation For Building Mapmentioning

confidence: 99%

Complexity of Atrial Fibrillation Electrograms Through Nonlinear Signal Analysis: In Silico Approach

Tobón¹,

Duque²,

Ugarte³

et al. 2017

Interpreting Cardiac Electrograms - From Skin to Endocardium

View full text Add to dashboard Cite

show abstract

“…Recently, the application of artificial neural networks (ANNs) in medical and nutritional sciences has been of great interest and raised hopes for better prevention, diagnosis and health care [24][25][26]. ANNs have been successfully applied in clinical trials s to predict the risk of dengue disease [27,28] or the level of cholesterol associated with body composition [29].…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Network to Somatotype Determination

2021

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Somatotype characteristics are important for the selection of sporting activities, as well as and the prevalence of several chronic diseases. Nowadays the most common method of somatotyping is the Heath–Carter method, which calculates the somatotype base on 10 anthropometric parameters. Another possibility for evaluation of somatotype gives commonly used bioelectrical impedance analysis), but the accuracy of the proposed formulas is questioned. Therefore, we aimed to investigate the possibility of applying an artificial neural network to achieve the formulas, which allow us to determine the endomorphy and mesomorphy using data on body height and weight and raw bioelectrical impedance analysis data in young women. The endomorphy (Endo), ectomorphy (Ecto), and mesomorphy (Meso) ratings were determined using artificial neural networks and the Heath–Carter method. To identify critical parameters and their degree of impact on the artificial neural network outputs, a sensitivity analysis was performed. The multi-layer perceptron MLP 4-4-1 (input: body mass index (BMI), reactance, resistance, and resting metabolic rate) for the Endo somatotype was proposed (root mean squared error (RMSE) = 0.66, χ2 = 0.66). The MLP 4-4-1 (input: BMI, fat-free mass, resistance, and total body water) for the Meso somatotype was proposed (RMSE = 0.76, χ2 = 0.87). All somatotypes (Endo, Meso and Ecto) can be calculated using MLP 2-4-3 (input: BMI and resistance) with accuracy RMSE = 0.67 and χ2 = 0.51. The bioelectrical impedance analysis and Heath–Carter method compliance was evaluated with the statistical algorithm proposed by Bland and Altman. The artificial neural network-based formulas allow us to determine the endomorphy and mesomorphy in young women’s ratings with high accuracy and agreement with the Heath–Carter method. The results of our study indicate the successful application of artificial neural network-based model in predicting the somatotype of young women. The artificial neural network model can be practically used in bioelectrical impedance analysis devices in the future.

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Performance of Ensemble Learning Models for Non-Invasive Total Cholesterol Level Estimation

Alghlayini,

Al-Betar

2023

2023 24th International Arab Conference on Information Technology (ACIT)

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Non-Invasive Approach to Predict the Cholesterol Level in Blood Using Bioimpedance and Neural Network Techniques

Cited by 6 publications

References 30 publications

Complexity of Atrial Fibrillation Electrograms Through Nonlinear Signal Analysis: In Silico Approach

Complexity of Atrial Fibrillation Electrograms Through Nonlinear Signal Analysis: In Silico Approach

Application of Artificial Neural Network to Somatotype Determination

Performance of Ensemble Learning Models for Non-Invasive Total Cholesterol Level Estimation

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