Journal Article

Classification of ECG signal using FFT based improved Alexnet classifier

Chakrapani, Arvind, Kumar M., Arun

2022

Overview

Electrocardiograms (ECG) are extensively used for the diagnosis of cardiac arrhythmias. This paper investigates the use of machine learning classification algorithms for ECG analysis and arrhythmia detection. This is a crucial component of a conventional electronic health system, and it frequently necessitates ECG signal reduction for long-term data storage and remote transmission. Signal processing methods must be used to extract the function of the morphological properties of the ECG signal changing with time, which is difficult to discern in the typical visual depiction of the ECG signal. In biomedical research, signal processing and data analysis are commonly employed methodologies. This work proposes the use of an ECG arrhythmia classification method based on Fast Fourier Transform (FFT) for feature extraction and an improved AlexNet classifier to distinguish the difference between four types of arrhythmia conditions that were collected from records. The Convolutional Neural Network (CNN) algorithm’s results are compared to those of other algorithms, and the simulation results prove that the proposed technique is more effective for various parameters. The final results of the proposed system show that its ability to find deviations is 20% better than that of traditional systems.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Algorithms / Analysis / Arrhythmia / Artificial neural networks / Biology and Life Sciences / Cardiovascular disease / Cardiovascular diseases / Care and treatment / Classification / Classifiers / Computer and Information Sciences / Data analysis / Data processing / Data storage / Deep learning / Diagnosis / EKG / Electrocardiogram / Electrocardiography / Engineering and Technology / Fast Fourier transformations / Feature extraction / Fourier transforms / Health aspects / Machine learning / Medical research / Medicine and Health Sciences / Methods / Neural networks / Physical Sciences / Research and Analysis Methods / Signal classification / Signal processing / Visual signals