Cardiac Signals Based Methods For Recognizing Heart Disease: A Review

Kumar, Prashanth; Sharma, Vijay K

doi:10.1109/icicv50876.2021.9388448

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…This time-domain parameter is used to calculate the averaged cubed deviation concerning the Standard deviation of the signal in the computation of variation under the mean of the selected portion (Kumar and Sharma 2021). This parameter is significantly presenting the abnormalities in the Standard value in bivariate mode.…”

Section: Skewnessmentioning

confidence: 99%

Recent advances in Ecological Aspect For prediction model based on ECG beat classification using neural network

2022

Global NEST: The International Journal

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<p>The ecological aspects such as environmental factors, socio-economic constraints, and demographic parameters are one of the key aspects of examining the health benefits of human subjects and are used as a ready reference in ecosystem modeling. Ecosystems are growing at an extraordinary speed around the world. Environmental factors are assumed to be responsible for over half of the global illness load. Environmental and ecological imbalances have a severe effect on public health, food security, global geopolitical and economic stability. The increased amount of environmental polluting factors eventually affects the health of humans and can cause diseases and disorders. Presently, there are various kinds of deadly diseases and disorders that are liable for affecting human health and impacting the eco framework of the whole world. Viruses such as corona, swine flu, omicron, and others are one of the best examples for the research community to understand the vulnerability of human health to these unpredictable causes. As per the report of the world health organization every year more than ten million people are affected by such ecological and environmental disbalance. The impact of environmental factors on the functioning of several organs in humans appears to be significant. There is a need to understand this ecological model with the health of human subjects. In this study, a cohort-based data set of ecological pollutants and physiological signals such as ECG and anthropogenic data of human subjects were extracted from Maharashtra from 2015 to 2021. As per neural network-based hazard ratio was calculated and observed to be deplorable among unhealthy and health categories of human subjects. The accumulative ecosystem is responsible for overburdening organs of living beings and policymakers must focus on the facts of study for modern management framework designs. Such initiatives could result in a more robust understanding of the human health implications of increasing environmental change, impacting decision-making in the environmental preservation and preventive health services.</p>

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Section: Skewnessmentioning

confidence: 99%

Recent advances in Ecological Aspect For prediction model based on ECG beat classification using neural network

2022

Global NEST: The International Journal

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Contrastive learning for atrial fibrillation detection in challenging scenarios

Fonseca

Osório

Castillo

et al. 2022

2022 30th European Signal Processing Conference (EUSIPCO)

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An Automatic ECG Signal Quality Assessment Method Based on Resnet and Self-Attention

et al. 2023

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Electrocardiogram (ECG) signals are among the significant physiological signals that indicate the essential properties of the human body. In recent years, the measurement of ECG signals has become more portable thanks to the increasing usage of wearable health testing technology. However, the enormous amount of signal data gathered over a long period of time does impose a heavy load on medical professionals. In addition, false alarms might occur due to the potential for the detected signal to become jumbled with noise and motion perturbations. Therefore, analyzing the quality of the measured raw ECG signal automatically is a valuable task. In this paper, we propose a new single-channel ECG signal quality assessment method that combines the Resnet network structure and the principle of self-attention to extract ECG signal features using the principle of similarity between individual QRS heartbeats within a time slice of ten seconds. In addition, an improved self-attention module is introduced into the deep neural network to learn the similarity between features. Finally, the network distinguishes between acceptable and unacceptable ECG segments. The model test results indicate that the F1-score can approach 0.954, which leads to a more accurate assessment of the ECG signal quality.

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Cardiac Signals Based Methods For Recognizing Heart Disease: A Review

Cited by 3 publications

References 22 publications

Recent advances in Ecological Aspect For prediction model based on ECG beat classification using neural network

Recent advances in Ecological Aspect For prediction model based on ECG beat classification using neural network

Contrastive learning for atrial fibrillation detection in challenging scenarios

An Automatic ECG Signal Quality Assessment Method Based on Resnet and Self-Attention

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