Genetic-algorithm cancellation of sinusoidal powerline interference in electrocardiograms

Kumaravel, N.; Nithiyanandam, N.

doi:10.1007/bf02510742

Cited by 21 publications

(6 citation statements)

References 12 publications

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“…(a) Unidimensional, binary genome [2,5,[7][8][9]20,22,24,25,29,31,33,42,53,58,60,70,72,73,83,85,86,88,98,99,108,110,115,125,138,153,154,[165][166][167]174,176,179,187,[189][190][191]197]. This is the most popular representation in genetic algorithms because it is simple to use and applicable to almost any problem [171].…”

Section: A32 According To the Evolutionary Techniquementioning

confidence: 99%

3 Coevolutionary Fuzzy Modeling

Reyes¹,

Andrés²

2004

Coevolutionary Fuzzy Modeling

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This thesis presents Fuzzy CoCo, a novel approach for system design, conducive to explaining human decisions. Based on fuzzy logic and coevolutionary computation, Fuzzy CoCo is a methodology for constructing systems able to accurately predict the outcome of a human decision-making process, while providing an understandable explanation of the underlying reasoning. Fuzzy logic provides a formal framework for constructing systems exhibiting both good numeric performance (precision) and linguistic representation (interpretability). From a numeric point of view, fuzzy systems exhibit nonlinear behavior and can handle imprecise and incomplete information. Linguistically, they represent knowledge in the form of rules, a natural way for explaining decision processes. Fuzzy modeling-meaning the construction of fuzzy systems-is an arduous task, demanding the identification of many parameters. This thesis analyses the fuzzy-modeling problem and different approaches to coping with it, focusing on evolutionary fuzzy modelingthe design of fuzzy inference systems using evolutionary algorithms-which constitutes the methodological base of my approach. In order to promote this analysis the parameters of a fuzzy system are classified into four categories: logic, structural, connective, and operational. The central contribution of this work is the use of an advanced evolutionary techniquecooperative coevolution-for dealing with the simultaneous design of connective and operational parameters. Cooperative coevolutionary fuzzy modeling succeeds in overcoming several limitations exhibited by other standard evolutionary approaches: stagnation, convergence to local optima, and computational costliness. Designing interpretable systems is a prime goal of my approach, which I study thoroughly herein. Based on a set of semantic and syntactic criteria, regarding the definition of linguistic concepts and their causal connections, I propose a number of strategies for producing more interpretable fuzzy systems. These strategies are implemented in Fuzzy CoCo, resulting in a modeling methodology providing high numeric precision, while incurring as little a loss of interpretability as possible. After testing Fuzzy CoCo on a benchmark problem-Fisher's Iris data-I successfully apply the algorithm to model the decision processes involved in two breast-cancer diagnostic problems: the WBCD problem and the Catalonia mammography interpretation problem. For the WBCD problem, Fuzzy CoCo produces systems both of high performance and high interpretability, comparable (if not better) than the best systems demonstrated to date. For the Catalonia problem, an evolved high-performance system was embedded within a web-based tool-called COBRA-for aiding radiologists in mammography interpretation. Several aspects of Fuzzy CoCo are thoroughly analyzed to provide a deeper understanding of the method. These analyses show the consistency of the results. They also help derive 1 "Y tuve muchos maestros de que aprender. Sólo conocían su ciencia y el deber." Charly García. A...

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Section: A32 According To the Evolutionary Techniquementioning

confidence: 99%

3 Coevolutionary Fuzzy Modeling

Reyes¹,

Andrés²

2004

Coevolutionary Fuzzy Modeling

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“…Yannis et al proposed a scheme according to the energy of the first-order IMF through which noise cancellation was performed among IMFs for ECG signals [20]. Kumaravel et al presented a genetic algorithm to determine the noise energy threshold in the first-order IMF for the PLn cancellation in ECG signals [21].…”

Section: Introductionmentioning

confidence: 99%

ECG Noise Cancellation Based on Grey Spectral Noise Estimation

Liu

Hsieh

Chen

et al. 2019

Sensors

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In recent years, wearable devices have been popularly applied in the health care field. The electrocardiogram (ECG) is the most used signal. However, the ECG is measured under a body-motion condition, which is easily coupled with some noise, like as power line noise (PLn) and electromyogram (EMG). This paper presents a grey spectral noise cancellation (GSNC) scheme for electrocardiogram (ECG) signals where two-stage discrimination is employed with the empirical mode decomposition (EMD), the ensemble empirical mode decomposition (EEMD) and the grey spectral noise estimation (GSNE). In the first stage of the proposed GSNC scheme, the input ECG signal is decomposed by the EMD to obtain a set of intrinsic mode functions (IMFs). Then, the noise energies of IMFs are estimated by the GSNE. When an IMF is considered as noisy one, it is forwarded to the second stage for further check. In the second stage, the suspicious IMFs are reconstructed and decomposed by the EEMD. Then the IMFs are discriminated with a threshold. If the IMF is considered as noisy, it is discarded in the reconstruction process of the ECG signal. The proposed GSNC scheme is justified by forty-three ECG signal datasets from the MIT-BIH cardiac arrhythmia database where the PLn and EMG noise are under consideration. The results indicate that the proposed GSNC scheme outperforms the traditional EMD and EEMD based noise cancellation schemes in the given datasets.

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“…Need for a reference signal and convergence to optimal solution are the major limitations in adaptive filter‐based methods. Different soft computing techniques such as genetic algorithm [8] and artificial neural network‐based [9] methods are proposed for removal of PLI from ECG signal. Soft computing techniques are not suitable for real‐time ECG analysis because of its computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Ensemble empirical mode decomposition‐based optimised power line interference removal algorithm for electrocardiogram signal

Jebaraj¹,

Arumugam²

2016

IET signal process.

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This study proposes an optimised algorithm to remove power line interference (PLI) from electrocardiogram (ECG) signal based on ensemble empirical mode decomposition (EEMD). A computationally efficient algorithm is one of the important requirements for real-time monitoring of cardio activities and diagnosis of arrhythmias. Computational complexity in EEMD is significantly reduced by using the EMD as the preprocessing stage. The noisy ECG signal is decomposed into intrinsic mode functions (IMFs) using EMD. ECG signals which are affected by PLI are automatically identified based on the simple ratio of the zero crossing number of IMF components. EEMD is used to decompose only ECG segments constructed from the noisy IMF components. The proposed algorithm is evaluated by real ECG signals available in MIT-BIH arrhythmia database in terms of signal-to-noise ratio and root mean square error. The computational efficiency of this new framework is measured using MATLAB profiling functions and compared with EMD, EEMD, sign-based adaptive and EMD with wavelet-based methods. Results show that the proposed algorithm performs better than the EMD, EEMD, sign-based adaptive and EMD with wavelet-based methods and it is computationally more efficient than EMD and EEMD methods.

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Genetic-algorithm cancellation of sinusoidal powerline interference in electrocardiograms

Cited by 21 publications

References 12 publications

3 Coevolutionary Fuzzy Modeling

3 Coevolutionary Fuzzy Modeling

ECG Noise Cancellation Based on Grey Spectral Noise Estimation

Ensemble empirical mode decomposition‐based optimised power line interference removal algorithm for electrocardiogram signal

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