Noise Reduction of ECG using Chebyshev filter and Classification using Machine Learning Algorithms

Prakash, M. Bhanu; Sowmya, .; Gopalakrishnan, E. A.

doi:10.1109/icccis51004.2021.9397163

Cited by 11 publications

(4 citation statements)

References 5 publications

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“…Geometric symmetry is a significant factor in digital signal processing applications, as evidenced by the utilization of finite discrete samples. There are two types of geometric symmetric are used: (i) N-point symmetric and (ii) N-point anti-symmetric 18,19 . The following condition should be satisfies for a length N-point symmetric response:…”

Section: Derived From the Principles Of Geometric Symmetrymentioning

confidence: 99%

“…Conducting numerous simulations with minor adjustments to the values of the control parameters, while adhering to the specified range indicated in the study. According to the literature 18 , in this study, the selection of parameters for each algorithm is conducted subsequent to thorough analysis and evaluation. Several simulations were conducted using a range of values as described in the previous research.…”

Section: Filter Design Using Gwomentioning

confidence: 99%

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Fine-tuning digital FIR filters with gray wolf optimization for peak performance

Samiappan,

Prabukumar

2024

Sci Rep

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The design of optimum filters constitutes a fundamental aspect within the realm of signal processing applications. The process entails the calculation of ideal coefficients for a filter in order to get a passband with a flat response and an unlimited level of attenuation in the stopband. The objective of this work is to solve the FIR filter design problem and to compare the optimal solutions obtained from evolutionary algorithms. The design of optimal FIR low pass (LP), high pass (HP), and band stop (BS) filters is achieved by the utilization of nature-inspired optimization approaches, namely gray wolf optimization ,cuckoo search, particle swarm optimization, and genetic algorithm. The filters are evaluated in terms of their stop band attenuation, pass band ripples, and departure from the anticipated response. In addition, this study compares the optimization strategies applied in the context of algorithm execution time which is achievement of global optimal outcomes for the design of digital finite impulse response (FIR) filters. The results indicate that when the Gray wolf algorithm is applied to the development of a finite impulse response (FIR) filter, it produces a higher level of performance than other approaches, as supported by enhanced design precision, decreased execution time, and achievement of an optimal solution.

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Section: Derived From the Principles Of Geometric Symmetrymentioning

confidence: 99%

Section: Filter Design Using Gwomentioning

confidence: 99%

Fine-tuning digital FIR filters with gray wolf optimization for peak performance

Samiappan,

Prabukumar

2024

Sci Rep

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“…Bhanu et al [11] proposed an algorithm for noise reduction of ECG. They extensively used the Chebyshev filter technique and ML based classification model.…”

Section: Literature Reviewmentioning

confidence: 99%

Meta-Learning Based Classification Model for Cardiovascular Disease

M¹,

Somasundaram²

2023

Preprint

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Cardiovascular disease is a major global health concern and is the leading cause of death and disability worldwide. According to the World Health Organization, cardiovascular disease is responsible for 17.9 million deaths each year, which accounts for 31% of all global deaths. Heart disease is a major cause of mortality worldwide. Machine learning algorithms have shown promise in predicting the risk of heart attacks. Meta-learning is a type of machine-learning method which enables a system to learn how to learn. It involves a set of techniques that allow a system to improve its own learning process. In this paper, we propose a Meta-learning based classification model for Cardiovascular diseases. We consider the dataset (for heart attack classification), which contains 76 attributes with the predicted attribute being the presence of heart disease. We evaluate traditional classification models and Meta-Learning approach for heart attack classification. Additionally, we compared the results using SMOTE and without SMOTE to balance the target classes. The Meta-learning approach outperforms traditional models, providing a more accurate prediction of heart attack risk. These results suggest that the meta-learning approach can be used to improve accuracy.

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“…There are only limited approaches that apply machine learning methods directly to ECG low noise solutions. The more appealing choice is implementing machine learning for feature-extracting, classifying, and symptom-diagnosing, using pure, low-noise signals as input [28,29]. Thus, it is suggested that if an efficient solution for ECG low noise using machine learning is provided, a complete system that takes the raw ECG signals as input and outputs low-noise signals with diagnosing results could be constructed and will become a considerable improvement.…”

Section: Filter Selectionmentioning

confidence: 99%

Highly Efficient Low Noise Solutions in ECG Signals

Liu

Yuan

2022

J. Phys.: Conf. Ser.

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This paper aims at helping researchers develop low-noise ECG monitors and analyze existing solutions for eliminating noise in ECG signals. A brief introduction of ECG signal, main kinds of noise in ECG signal and a summary of different kinds of low noise solutions is given. The components of the ECG signal and the origin of noise in the ECG signal are analyzed first. In order to find optimized solutions for eliminating the noise, highly effective solutions are listed and analyzed. Solutions are divided into three main directions, which are circuit design, signal processing and machine learning. Specific practical examples from former research and analyses for each direction help the reader understand and select different ways of eliminating noise in ECG signals. After introducing three directions, a summary and further improvement are given to help readers have a horizontal comparison between solutions.

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Noise Reduction of ECG using Chebyshev filter and Classification using Machine Learning Algorithms

Cited by 11 publications

References 5 publications

Fine-tuning digital FIR filters with gray wolf optimization for peak performance

Fine-tuning digital FIR filters with gray wolf optimization for peak performance

Meta-Learning Based Classification Model for Cardiovascular Disease

Highly Efficient Low Noise Solutions in ECG Signals

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