Optimization of Multi-layer Perceptron Neural Network Using Genetic Algorithm for Arrhythmia Classification

Kumari, V. S. R.

doi:10.11648/j.com.20150305.21

Cited by 10 publications

(2 citation statements)

References 33 publications

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“…63 In recent study, algorithms and machine learning have been used to identify and classify cardiac arrhythmias by interpreting ECG information. 64 Another study used an optimisation genetic algorithm (GA) and a support vector machine (SVM) classifier to efficiently classify and diagnose TB. 65…”

Section: Ai In Disease Diagnosismentioning

confidence: 99%

Transformative trends: A comprehensive review on role of artificial intelligence in healthcare and pharmaceutical research

Bhabad,

Lamkhade,

Koyate

et al. 2023

IJCAAP

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Artificial intelligence (AI) represents a specialized field within computer science, empowering machines to efficiently process and analyze intricate data. The surge in AI-focused research has notably accelerated, particularly in its growing role within healthcare services and research. This review delves into the manifold opportunities and challenges that AI presents in the realms of healthcare and pharmaceutical research. To compile relevant literature, a comprehensive search was conducted across databases such as PubMed, Science Direct, and Google Scholar. The article extensively explores the application of AI in diverse healthcare aspects such as disease diagnosis, digital therapy, personalized treatment, drug discovery, and the anticipation of epidemics or pandemics. The predominant AI technologies employed include deep learning and neural networks. The review emphasizes the pivotal role of advancing AI technologies in fostering rapid and cost-effective healthcare and pharmaceutical research. The anticipated outcome is an improved delivery of services to the general public, reflecting the transformative impact of AI on scientific endeavors.

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Section: Ai In Disease Diagnosismentioning

confidence: 99%

Transformative trends: A comprehensive review on role of artificial intelligence in healthcare and pharmaceutical research

Bhabad,

Lamkhade,

Koyate

et al. 2023

IJCAAP

View full text Add to dashboard Cite

show abstract

“…The change of classifier namely the Multilayer Perceptron (MP) along with GA was more efficient in detecting cardiac arrhythmias. The extraction of R-R intervals was done by using Symlet which also helped in the reduction of uncertain features (Kumari and Kumar, 2015). Inan et al (2006) projected an approach for grouping the beats of an enormous data set utilizing a wavelet and time function.…”

Section: Introductionmentioning

confidence: 99%

Cardiovascular Diseases Identification Using Wavelet Optimization and Modify Cuckoo Search Algorithm

Negi,

Bartwal,

Verma

et al. 2023

Journal of Computer Science

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The problem the author aims to solve is the extraction of discriminatory features in ECG (Electrocardiogram) signals for classification purposes. The scope of the work is to propose a new method for building wavelets that best reflect the discriminatory capacity of ECG signals. The approach involves optimizing the wavelets specifically for the classification function under consideration. To address the problem, the author proposes a novel method for creating wavelets that optimize discriminatory feature extraction in ECG signals. The approach utilizes the poly-phase demonstration of the filter bank and incorporates the Modified Cuckoo Search (MCS) algorithm to project the problem context. The experiments are conducted using the MIT/BIH arrhythmia database to evaluate the performance of the proposed method against existing state-ofthe-art techniques. The Support Vector Machine (SVM) classifier is used to demonstrate the effectiveness, precision, and robustness of the projected strategy on standard wavelets like Daubechies and Symlet. The extent of the author's work involves developing a new method for wavelet construction to enhance discriminatory feature extraction in ECG signals. Important variables controlled in the study include the choice of wavelet parameters, the application of the MCS algorithm, and the evaluation of results against standard wavelet-based classification methods. The experiment results demonstrate the superiority of the proposed wavelet construction method over traditional wavelets like Daubechies and Symlet for ECG signal classification. The new wavelet shows improved discriminatory capacity, leading to higher classification precision and accuracy. The findings imply that optimizing wavelets for the classification of ECG signals using the Modified Cuckoo Search algorithm can significantly enhance discriminatory feature extraction and improve classification precision. The results are potentially generalizable and can be applied to various ECG signal classification tasks, contributing to advancements in medical diagnostics and monitoring. Background: The extraction of discriminatory features from ECG signals is a critical task in the field of medical signal processing. Accurate classification of these features plays a crucial role in diagnosing various cardiac arrhythmias and abnormalities. Wavelet-based techniques have shown promise in this domain, but further optimizations are necessary to achieve higher classification precision and robustness. Current Status in the Field: The field of ECG signal classification continues to evolve, with researchers exploring various approaches to improve discriminatory feature extraction. Wavelet-based methods have gained popularity due to their ability to capture both frequency and time-domain information, but fine-tuning the

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Human machine interfacing technique for diagnosis of ventricular arrhythmia using supervisory machine learning algorithms

Karnan

Sivakumaran

Manivel³

2018

Concurrency and Computation

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Summary The state of art to integrate bio‐signals with computer based diagnosis is taking dominance. The man‐machine interface is useful for early and immediate clinical interpretation. The electrocardiogram (ECG) signal plays a vital role in revealing the possible data towards categorizing normal and abnormal cardiac functioning. The fatal conditions exhibited by ventricular arrhythmias (VA) pose a remarkable change in the feature set of the ECG signals. In this work, a novel approach to segregate the superior feature toward the ventricular arrhythmias are extracted using feature ranking score algorithm (FRSA). The FRSA collects feature vectors in three different domains and ranks it to find out the more prevalent feature for diagnosis of VA. The Support Vector Machine (SVM) classifier is administered by supervisory machine learning optimization algorithm Mean Grey Wolf Optimization (MGWO). The performance estimates of SVM‐MGWO is compared for classification of VA signals with other optimization also like SVM‐Particle Swarm Optimization (SVM‐PSO) and SVM‐Grey Wolf Optimization (SVM‐GWO). The non‐parametric and parametric analysis evidently shows the improved performance of feature parameter estimates for classification. The accuracy of classification for SVM‐MGWO attains 100% for finding test data with VA at a minimal convergence iteration while comparing it with the other mentioned supervisory algorithms. The standard deviation during parametric analysis is negligible, which reveals the fact that reductant feature extracted and utilized for testing of ECG data is minimal. The performance estimates attained by the proposed algorithm shows the selection of optimal feature for the findings of VA through ECG. The man‐machine interface aides in the early diagnosis of ventricular arrhythmias using non‐invasive diagnosing tool, the ECG.

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Optimization of Multi-layer Perceptron Neural Network Using Genetic Algorithm for Arrhythmia Classification

Cited by 10 publications

References 33 publications

Transformative trends: A comprehensive review on role of artificial intelligence in healthcare and pharmaceutical research

Transformative trends: A comprehensive review on role of artificial intelligence in healthcare and pharmaceutical research

Cardiovascular Diseases Identification Using Wavelet Optimization and Modify Cuckoo Search Algorithm

Human machine interfacing technique for diagnosis of ventricular arrhythmia using supervisory machine learning algorithms

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