Computer methods and programs in biomedicine
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Comput Methods Programs Biomed · Aug 2012
Monte Carlo simulation of parameter confidence intervals for non-linear regression analysis of biological data using Microsoft Excel.
This study describes a method to obtain parameter confidence intervals from the fitting of non-linear functions to experimental data, using the SOLVER and Analysis ToolPaK Add-In of the Microsoft Excel spreadsheet. Previously we have shown that Excel can fit complex multiple functions to biological data, obtaining values equivalent to those returned by more specialized statistical or mathematical software. ⋯ Using a simple Monte-Carlo procedure within the Excel spreadsheet (without recourse to programming), SOLVER can provide parameter estimates (up to 200 at a time) for multiple 'virtual' data sets, from which the required confidence intervals and correlation coefficients can be obtained. The general utility of the method is exemplified by applying it to the analysis of the growth of Listeria monocytogenes, the growth inhibition of Pseudomonas aeruginosa by chlorhexidine and the further analysis of the electrophysiological data from the compound action potential of the rodent optic nerve.
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Comput Methods Programs Biomed · Aug 2012
Wiener sliding-mode control for artificial pancreas: a new nonlinear approach to glucose regulation.
Type 1 diabetic patients need insulin therapy to keep their blood glucose close to normal. In this paper an attempt is made to show how nonlinear control-oriented model may be used to improve the performance of closed-loop control of blood glucose in diabetic patients. The nonlinear Wiener model is used as a novel modeling approach to be applied to the glucose control problem. ⋯ To improve the meal compensation features, the controllers are provided with a simple feedforward controller to inject an insulin bolus at meal time. Different simulation scenarios have been used to evaluate the proposed controllers. The obtained results show that the new approach outperforms the linear control scheme, and regulates the glucose level within safe limits in the presence of measurement and modeling errors, meal uncertainty and patient variations.
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Comput Methods Programs Biomed · Mar 2012
Feature extraction for ECG heartbeats using higher order statistics of WPD coefficients.
This paper describes feature extraction methods using higher order statistics (HOS) of wavelet packet decomposition (WPD) coefficients for the purpose of automatic heartbeat recognition. The method consists of three stages. First, the wavelet package coefficients (WPC) are calculated for each different type of ECG beat. ⋯ All heartbeats in the arrhythmia database are grouped into five main heartbeat classes. The classification accuracy of the proposed system is measured by average sensitivity of 90%, average selectivity of 92% and average specificity of 98%. The results show that HOS of WPC as features are highly discriminative for the classification of different arrhythmic ECG beats.
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Comput Methods Programs Biomed · Dec 2011
Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms.
Improving accuracies of machine learning algorithms is vital in designing high performance computer-aided diagnosis (CADx) systems. Researches have shown that a base classifier performance might be enhanced by ensemble classification strategies. In this study, we construct rotation forest (RF) ensemble classifiers of 30 machine learning algorithms to evaluate their classification performances using Parkinson's, diabetes and heart diseases from literature. ⋯ Base classifiers succeeded 72.15%, 77.52% and 84.43% average accuracies for diabetes, heart and Parkinson's datasets, respectively. As for RF classifier ensembles, they produced average accuracies of 74.47%, 80.49% and 87.13% for respective diseases. RF, a newly proposed classifier ensemble algorithm, might be used to improve accuracy of miscellaneous machine learning algorithms to design advanced CADx systems.
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Comput Methods Programs Biomed · Dec 2011
The Intelligent Ventilator (INVENT) project: the role of mathematical models in translating physiological knowledge into clinical practice.
This dissertation has addressed the broad hypothesis as to whether building mathematical models is useful as a tool for translating physiological knowledge into clinical practice. In doing so it describes work on the INtelligent VENTilator project (INVENT), the goal of which is to build, evaluate and integrate into clinical practice, a model-based decision support system for control of mechanical ventilation. The dissertation describes the mathematical models included in INVENT, i.e. a model of pulmonary gas exchange focusing on oxygen transport, and a model of the acid-base status of blood, interstitial fluid and tissues. ⋯ The ALPE model and system has been applied in intensive care, post operative care and cardiology and is currently being evaluated in new clinical domains. ARTY has been shown to have potential benefit in eliminating the need for painful arterial punctures, and may also be useful as a screening tool. These systems illustrate the benefits of investing in models as a mechanism for translating physiological knowledge to clinical practice.