Early prediction of Cardiovascular Diseases using ECG signal: Review

Kamaruddin, Nurul Hikmah; Murugappan, M.; Omar, Mohammad Iqbal

doi:10.1109/scored.2012.6518609

Cited by 15 publications

(2 citation statements)

References 23 publications

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“…Heart attack, angina, and stroke are the primary cardiovascular illnesses. Heart problems, or CVD, refer to the heart's malfunction and dysfunction, which ultimately results in a reduction in the amount of oxygen delivered to the body's important functions [1]. Risk factors like smoking have an impact on how the heart naturally beats and produces electrical impulses.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Feature Selection and Classifying Stages through Electrocardiogram (ECG) Signal for Heart Disease Prediction

Kumar,

Soundararajan,

Natesan

et al. 2023

RAiSE-2023

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Section: Introductionmentioning

confidence: 99%

Hybrid Feature Selection and Classifying Stages through Electrocardiogram (ECG) Signal for Heart Disease Prediction

Kumar,

Soundararajan,

Natesan

et al. 2023

RAiSE-2023

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“…With ECG data, arrhythmia identification can be made using computer-aided diagnosis systems [2]. Recent advancements in the computer field, such as enhanced GPUs and computer vision and the availability of datasets, have made it possible to identify trends in ECG which indicate these diseases [4]. The use of neural networks has made it fast, easy, and cheap to give accurate results.…”

Section: Introductionmentioning

confidence: 99%

ECG Data Analysis with Denoising Approach and Customized CNNs

Mishra

Dharahas

Gite

et al. 2022

Sensors

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In the last decade, the proactive diagnosis of diseases with artificial intelligence and its aligned technologies has been an exciting and fruitful area. One of the areas in medical care where constant monitoring is required is cardiovascular diseases. Arrhythmia, one of the cardiovascular diseases, is generally diagnosed by doctors using Electrocardiography (ECG), which records the heart’s rhythm and electrical activity. The use of neural networks has been extensively adopted to identify abnormalities in the last few years. It is found that the probability of detecting arrhythmia increases if the denoised signal is used rather than the raw input signal. This paper compares six filters implemented on ECG signals to improve classification accuracy. Custom convolutional neural networks (CCNNs) are designed to filter ECG data. Extensive experiments are drawn by considering the six ECG filters and the proposed custom CCNN models. Comparative analysis reveals that the proposed models outperform the competitive models in various performance metrics.

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A Risk Assessment Model for Patients Suffering from Coronary Heart Disease Using a Novel Feature Selection Algorithm and Learning Classifiers

Joshi¹,

Nair

2020

Advances in Intelligent Systems and Computing

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Early prediction of Cardiovascular Diseases using ECG signal: Review

Cited by 15 publications

References 23 publications

Hybrid Feature Selection and Classifying Stages through Electrocardiogram (ECG) Signal for Heart Disease Prediction

Hybrid Feature Selection and Classifying Stages through Electrocardiogram (ECG) Signal for Heart Disease Prediction

ECG Data Analysis with Denoising Approach and Customized CNNs

A Risk Assessment Model for Patients Suffering from Coronary Heart Disease Using a Novel Feature Selection Algorithm and Learning Classifiers

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