Training feedforward neural network using genetic algorithm to diagnose left ventricular hypertrophy Article Swipe

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J. Revathi , J. Anitha , D. Jude Hemanth ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.12928/telkomnika.v18i3.15225

In this research work, a new technique was proposed for the diagnosis of left ventricular hypertrophy (LVH) from the ECG signal. The advanced imaging techniques can be used to diagnose left ventricular hypertrophy, but it leads to time-consuming and more expensive. This proposed technique overcomes thesef issues and may serve as an efficient tool to diagnose the LVH disease. The LVH causes changes in the patterns of ECG signal which includes R wave, QRS and T wave. This proposed approach identifies the changes in the pattern and extracts the temporal, spatial and statistical features of the ECG signal using windowed filtering technique. These features were applied to the conventional classifier and also to the neural network classifier with the modified weights using a genetic algorithm. The weights were modified by combining the crossover operators such as crossover arithmetic and crossover two-point operator. The results were compared with the various classifiers and the performance of the neural network with the modified weights using a genetic algorithm is outperformed. The accuracy of the weights modified feedforward neural network is 97.5%.

Related Topics

Computer Science

Feedforward Neural Network

Qrs Complex

Genetic Algorithm

Algorithm

Artificial Intelligence

Feed Forward (Control)

Left Ventricular Hypertrophy

Concepts

Crossover Artificial neural network Computer science Feedforward neural network Pattern recognition (psychology) QRS complex Classifier (UML) Genetic algorithm Algorithm Artificial intelligence Feed forward Left ventricular hypertrophy Machine learning Medicine Cardiology Internal medicine Engineering Control engineering Blood pressure

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Type: article
Language: en
Landing Page: https://doi.org/10.12928/telkomnika.v18i3.15225
PDF: http://journal.uad.ac.id/index.php/TELKOMNIKA/article/download/15225/8129
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Cited By: 10
Related Works: 10
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DOI: https://doi.org/10.12928/telkomnika.v18i3.15225

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Title: Training feedforward neural network using genetic algorithm to diagnose left ventricular hypertrophy

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Language: en

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Publication year: 2020

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Publication date: 2020-06-01

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Authors: J. Revathi, J. Anitha, D. Jude Hemanth

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Landing page: https://doi.org/10.12928/telkomnika.v18i3.15225

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Concepts: Crossover, Artificial neural network, Computer science, Feedforward neural network, Pattern recognition (psychology), QRS complex, Classifier (UML), Genetic algorithm, Algorithm, Artificial intelligence, Feed forward, Left ventricular hypertrophy, Machine learning, Medicine, Cardiology, Internal medicine, Engineering, Control engineering, Blood pressure

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Cited by: 10

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Citations by year (recent): 2025: 1, 2024: 1, 2023: 1, 2022: 2, 2021: 3

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