A Fuzzy Based Approach for Denoising of ECG Signal using Wavelet Transform

Goel, Supriya; Tomar, Pradeep; Kaur, Gurjit

doi:10.14257/ijbsbt.2016.8.2.13

Cited by 16 publications

(5 citation statements)

References 15 publications

(11 reference statements)

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“…Furthermore, to refine the classification system for the diagnosis of arrhythmia, a random forest ensemble method has been proposed which is based on a resampling strategy [11]. Various ML algorithms like gradient boosting, neural networks, random forests, decision trees, and support vector machines are used, after applying feature selection strategies and rigorous pre-processing on ECG data, for arrhythmia classification [12]. Likewise, medical ECG dataset has been treated by algorithms like J48, OneR, Naïve Bayes, support vector machine, K-nearest neighbor, random forest, logistic regression, and decision trees to categorize arrhythmia into sixteen different classes [13].…”

Section: Literature Surveymentioning

confidence: 99%

Classification of Cardiac Arrhythmia using improved Feature Selection methods and Ensemble Classifiers

Jain

Betrabet

Rao

et al. 2022

J. Phys.: Conf. Ser.

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Arrhythmia is one of the life-threatening heart diseases which is diagnosed and analyzed using electrocardiogram (ECG) recordings and other symptoms namely rapid heartbeat or chest-pounding, shortness of breath, near fainting spells, insufficient pumping of blood from the heart, etc along with sudden cardiac arrest. Arrhythmia records a hasty and aberrant ECG. In this implementation, the arrhythmia dataset is collected from the UCI machine learning repository and then classified the records into sixteen stated classes using multiclass classification. The large feature set of the dataset is reduced using improved feature selection techniques such as t-Distributed Stochastic Neighbor Embedding (TSNE), Principal Component Analysis (PCA), Uniform Manifold Approximation, and Projection (UMAP) and then an Ensemble Classifier is built to analyse the classification accuracy on arrhythmia dataset to conclude when and which approach gives optimal results.

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Section: Literature Surveymentioning

confidence: 99%

Classification of Cardiac Arrhythmia using improved Feature Selection methods and Ensemble Classifiers

Jain

Betrabet

Rao

et al. 2022

J. Phys.: Conf. Ser.

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“…ROM is an abbreviation for read-only memory that can only read and cannot write information. Erasable programmable ROM uses ultraviolet rays to inject and remove hot electrons from the floating gate of the memory cell to achieve memory programming and erasing operations [19]. Both electrically erasable PROM and flash memory belong to electrically erasable nonvolatile memory; PROM is short for programmable read-only memory.…”

Section: Nonvolatile Memory Programming Mechanismmentioning

confidence: 99%

A New Type of ECG Signal Acquisition and Storage Nonvolatile Chip Embedded in Mobile Devices for Sports Monitoring

Zhang

2022

Journal of Nanomaterials

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In the fast-paced development of life in today’s society, the use of ECG signals is not only used in the medical direction to scan patients; portable ECG signal acquisition tools can collect and analyze the corresponding ECG signals in time and respond to the wearer in time, so that the user can make timely adjustments or seek medical treatment in time to avoid a malignant situation that occurs. For people in sports, the collection of ECG signals is more helpful to the arrangement and estimation of training conditions. Based on nonvolatile memory technology, data will not be lost when the computer is turned off or suddenly or unexpectedly shut down. This research puts forward the design of an antiexercise ECG monitoring system that collects ECG signals for a group that is in the process of exercise. Use mathematical calculation system to classify, and conduct in-depth analysis of the reasons for poor anti-interference ability, and use the original denoising algorithm calculation as the basis to adapt to the dynamic situation in the opposite situation to the normal ECG signal acquisition in the static state. The collection is modified from the original basis. This research proposes an improved scheme for multichannel acquisition. Chips of semiconductor memory devices based on nonvolatile memory are embedded in mobile devices for use. The experimental results in this paper show that the comparison experiment ratio of collecting ECG signals from the sixth, third, fourth, and fifth directions can effectively reduce the mean square error (MSE), and the error of channel 6 is directly from 0.1598. It is down to 0.00143. Several other items showed a significant decrease. It shows that the anti-interference ability of the ECG monitoring system can be further enhanced by increasing the number of collection terminals.

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“…This review, therefore, is an attempt to critically explore the literature in the area of different algorithms and machine learning techniques in ECG analysis. ECG is widely used in the diagnosis of heart diseases, where fuzzy inference systems can be used for effective system analysis (Goel et al, 2016). The research on fuzzy based techniques have further developed through the incorporation of neural networks, such as fuzzy inference networks (Lei et al, 2007), that integrates the benefits of neural networks for better learning and fuzzy sets for better human understanding (Guler & Ubeyli, 2004).…”

Section: Literature Surveymentioning

confidence: 99%

A Survey on various Machine Learning Approaches for ECG Analysis

Roopa¹,

Harish²

2017

IJCA

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Electrocardiogram (ECG) is a P, QRS and T wave demonstrating the electrical activity of the heart. Feature extraction and segmentation in ECG plays a significant role in diagnosing most of the cardiac disease. The main objective of this paper is to review the various machine learning approaches for diagnosing Myocardial Infarction (heart attack), differentiate Arrhythmias (heart beat variation), Hypertrophy (increase thickness of the heart muscle) and Enlargement of Heart. Further, we also present various machine learning approaches and compare different methods and results used to analyze the ECG. The existing methods are compared and contrasted based on qualitative and qualitative parameters viz., purpose of the work, algorithms adopted and results obtained.

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A Fuzzy Based Approach for Denoising of ECG Signal using Wavelet Transform

Abstract: Electrocardiogram (ECG) is a vital biomedical signal for diagnosing heart diseases

Cited by 16 publications

References 15 publications

Classification of Cardiac Arrhythmia using improved Feature Selection methods and Ensemble Classifiers

Classification of Cardiac Arrhythmia using improved Feature Selection methods and Ensemble Classifiers

A New Type of ECG Signal Acquisition and Storage Nonvolatile Chip Embedded in Mobile Devices for Sports Monitoring

A Survey on various Machine Learning Approaches for ECG Analysis

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