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Displaying publications 81 - 85 of 85 in total

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Application of nonlinear methods to discriminate fractionated electrograms in paroxysmal versus persistent atrial fibrillation

Acharya UR, Faust O, Ciaccio EJ, Koh JEW, Oh SL, Tan RS, et al.

Comput Methods Programs Biomed, 2019 Jul;175:163-178.
PMID: 31104705 DOI: 10.1016/j.cmpb.2019.04.018

BACKGROUND AND OBJECTIVE: Complex fractionated atrial electrograms (CFAE) may contain information concerning the electrophysiological substrate of atrial fibrillation (AF); therefore they are of interest to guide catheter ablation treatment of AF. Electrogram signals are shaped by activation events, which are dynamical in nature. This makes it difficult to establish those signal properties that can provide insight into the ablation site location. Nonlinear measures may improve information. To test this hypothesis, we used nonlinear measures to analyze CFAE.
METHODS: CFAE from several atrial sites, recorded for a duration of 16 s, were acquired from 10 patients with persistent and 9 patients with paroxysmal AF. These signals were appraised using non-overlapping windows of 1-, 2- and 4-s durations. The resulting data sets were analyzed with Recurrence Plots (RP) and Recurrence Quantification Analysis (RQA). The data was also quantified via entropy measures.
RESULTS: RQA exhibited unique plots for persistent versus paroxysmal AF. Similar patterns were observed to be repeated throughout the RPs. Trends were consistent for signal segments of 1 and 2 s as well as 4 s in duration. This was suggestive that the underlying signal generation process is also repetitive, and that repetitiveness can be detected even in 1-s sequences. The results also showed that most entropy metrics exhibited higher measurement values (closer to equilibrium) for persistent AF data. It was also found that Determinism (DET), Trapping Time (TT), and Modified Multiscale Entropy (MMSE), extracted from signals that were acquired from locations at the posterior atrial free wall, are highly discriminative of persistent versus paroxysmal AF data.
CONCLUSIONS: Short data sequences are sufficient to provide information to discern persistent versus paroxysmal AF data with a significant difference, and can be useful to detect repeating patterns of atrial activation.
Optimal control strategies for dengue fever spread in Johor, Malaysia

Abidemi A, Aziz NAB

Comput Methods Programs Biomed, 2020 Nov;196:105585.
PMID: 32554024 DOI: 10.1016/j.cmpb.2020.105585

Background Dengue is a vector-borne viral disease endemic in Malaysia. The disease is presently a public health issue in the country. Hence, the use of mathematical model to gain insights into the transmission dynamics and derive the optimal control strategies for minimizing the spread of the disease is of great importance. Methods A model involving eight mutually exclusive compartments with the introduction of personal protection, larvicide and adulticide control strategies describing dengue fever transmission dynamics is presented. The control-induced basic reproduction number (R˜0) related to the model is computed using the next generation matrix method. Comparison theorem is used to analyse the global dynamics of the model. The model is fitted to the data related to the 2012 dengue outbreak in Johor, Malaysia, using the least-squares method. In a bid to optimally curtail dengue fever propagation, we apply optimal control theory to investigate the effect of several control strategies of combination of optimal personal protection, larvicide and adulticide controls on dengue fever dynamics. The resulting optimality system is simulated in MATLAB using fourth order Runge-Kutta scheme based on the forward-backward sweep method. In addition, cost-effectiveness analysis is performed to determine the most cost-effective strategy among the various control strategies analysed. Results Analysis of the model with control parameters shows that the model has two disease-free equilibria, namely, trivial equilibrium and biologically realistic disease-free equilibrium, and one endemic equilibrium point. It also reveals that the biologically realistic disease-free equilibrium is both locally and globally asymptotically stable whenever the inequality R˜0<1holds. In the case of model with time-dependent control functions, the optimality levels of the three control functions required to optimally control dengue disease transmission are derived. Conclusion We conclude that dengue fever transmission can be curtailed by adopting any of the several control strategies analysed in this study. Furthermore, a strategy which combines personal protection and adulticide controls is found to be the most cost-effective control strategy.
Dynamic ECG features for atrial fibrillation recognition

Abdul-Kadir NA, Mat Safri N, Othman MA

Comput Methods Programs Biomed, 2016 Nov;136:143-50.
PMID: 27686711 DOI: 10.1016/j.cmpb.2016.08.021

BACKGROUND: Atrial fibrillation (AF) can cause the formation of blood clots in the heart. The clots may move to the brain and cause a stroke. Therefore, this study analyzed the ECG features of AF and normal sinus rhythm signals for AF recognition which were extracted by using a second-order dynamic system (SODS) concept.
OBJECTIVE: To find the appropriate windowing length for feature extraction based on SODS and to determine a machine learning method that could provide higher accuracy in recognizing AF.
METHOD: ECG features were extracted based on a dynamic system (DS) that uses a second-order differential equation to describe the short-term behavior of ECG signals according to the natural frequency (ω), damping coefficient, (ξ), and forcing input (u). The extracted features were windowed into 2, 3, 4, 6, 8, and 10 second episodes to find the appropriate windowing size for AF signal processing. ANOVA and t-tests were used to determine the significant features. In addition, pattern recognition machine learning methods (an artificial neural network (ANN) and a support vector machine (SVM)) with k-fold cross validation (k-CV) were used to develop the ECG recognition system.
RESULTS: Significant differences (p
A new machine learning technique for an accurate diagnosis of coronary artery disease

Abdar M, Książek W, Acharya UR, Tan RS, Makarenkov V, Pławiak P

Comput Methods Programs Biomed, 2019 Oct;179:104992.
PMID: 31443858 DOI: 10.1016/j.cmpb.2019.104992

BACKGROUND AND OBJECTIVE: Coronary artery disease (CAD) is one of the commonest diseases around the world. An early and accurate diagnosis of CAD allows a timely administration of appropriate treatment and helps to reduce the mortality. Herein, we describe an innovative machine learning methodology that enables an accurate detection of CAD and apply it to data collected from Iranian patients.
METHODS: We first tested ten traditional machine learning algorithms, and then the three-best performing algorithms (three types of SVM) were used in the rest of the study. To improve the performance of these algorithms, a data preprocessing with normalization was carried out. Moreover, a genetic algorithm and particle swarm optimization, coupled with stratified 10-fold cross-validation, were used twice: for optimization of classifier parameters and for parallel selection of features.
RESULTS: The presented approach enhanced the performance of all traditional machine learning algorithms used in this study. We also introduced a new optimization technique called N2Genetic optimizer (a new genetic training). Our experiments demonstrated that N2Genetic-nuSVM provided the accuracy of 93.08% and F1-score of 91.51% when predicting CAD outcomes among the patients included in a well-known Z-Alizadeh Sani dataset. These results are competitive and comparable to the best results in the field.
CONCLUSIONS: We showed that machine-learning techniques optimized by the proposed approach, can lead to highly accurate models intended for both clinical and research use.
Interpretation of radiomics features-A pictorial review

Abbasian Ardakani A, Bureau NJ, Ciaccio EJ, Acharya UR

Comput Methods Programs Biomed, 2022 Mar;215:106609.
PMID: 34990929 DOI: 10.1016/j.cmpb.2021.106609

Radiomics is a newcomer field that has opened new windows for precision medicine. It is related to extraction of a large number of quantitative features from medical images, which may be difficult to detect visually. Underlying tumor biology can change physical properties of tissues, which affect patterns of image pixels and radiomics features. The main advantage of radiomics is that it can characterize the whole tumor non-invasively, even after a single sampling from an image. Therefore, it can be linked to a "digital biopsy". Physicians need to know about radiomics features to determine how their values correlate with the appearance of lesions and diseases. Indeed, physicians need practical references to conceive of basics and concepts of each radiomics feature without knowing their sophisticated mathematical formulas. In this review, commonly used radiomics features are illustrated with practical examples to help physicians in their routine diagnostic procedures.