Best LVH-ECG criteria for Indonesian hypertensives

Nurdin, Wira Bahari; Januari, H.; Suryani, Sri; Dewang, Syamsir

doi:10.1088/1742-6596/1242/1/012036

Cited by 1 publication

(1 citation statement)

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“…It alters ventricle repolarization and depolarization which leads to the changes in QRS and T patterns in the ECG signal [5]. The various LVH-ECG criteria were reported which includes Cornell Voltages, Romhillt Estees and Sokolov Lyon (SL) [6]. The ECG is a common method on the account of wide availability [7].…”

Section: Introductionmentioning

confidence: 99%

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

Revathi¹,

Anitha

Hemanth

2020

TELKOMNIKA

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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%.

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Section: Introductionmentioning

confidence: 99%