A comprehensive survey on the biomedical signal processing methods for the detection of COVID-19

Anand, Satyajit; Sharma, Vikrant; Pourush, Rajeev; Jaiswal, Sandeep

doi:10.1016/j.amsu.2022.103519

Cited by 5 publications

(3 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The output of the Gammatone filterbanks is used in achieving the cochleagram, which is a representation of a frequency-time signal. Therefore, the impulse response for each gammatone filter can be expressed mathematically in Equation (1).…”

Section: Gammatone Frequency Cepstral Coefficients (Gfcc)mentioning

confidence: 99%

“…The coronavirus (COVID-19) pandemic can be described as a respiratory infection majorly caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and has infected more than 44 million individuals globally [1]. The effect of this 21st-century pandemic has negatively affected global economic activities such as finance [2], security [3], food security, education, and global peace [4], with some positive results in reducing urban pollution [5].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detection of COVID-19 from Deep Breathing Sounds Using Sound Spectrum with Image Augmentation and Deep Learning Techniques

et al. 2022

View full text Add to dashboard Cite

The COVID-19 pandemic is one of the most disruptive outbreaks of the 21st century considering its impacts on our freedoms and social lifestyle. Several methods have been used to monitor and diagnose this virus, which includes the use of RT-PCR test and chest CT/CXR scans. Recent studies have employed various crowdsourced sound data types such as coughing, breathing, sneezing, etc., for the detection of COVID-19. However, the application of artificial intelligence methods and machine learning algorithms on these sound datasets still suffer some limitations such as the poor performance of the test results due to increase of misclassified data, limited datasets resulting in the overfitting of deep learning methods, the high computational cost of some augmentation models, and varying quality feature-extracted images resulting in poor reliability. We propose a simple yet effective deep learning model, called DeepShufNet, for COVID-19 detection. A data augmentation method based on the color transformation and noise addition was used for generating synthetic image datasets from sound data. The efficiencies of the synthetic dataset were evaluated using two feature extraction approaches, namely Mel spectrogram and GFCC. The performance of the proposed DeepShufNet model was evaluated using a deep breathing COSWARA dataset, which shows improved performance with a lower misclassification rate of the minority class. The proposed model achieved an accuracy, precision, recall, specificity, and f-score of 90.1%, 77.1%, 62.7%, 95.98%, and 69.1%, respectively, for positive COVID-19 detection using the Mel COCOA-2 augmented training datasets. The proposed model showed an improved performance compared to some of the state-of-the-art-methods.

show abstract

Section: Gammatone Frequency Cepstral Coefficients (Gfcc)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection of COVID-19 from Deep Breathing Sounds Using Sound Spectrum with Image Augmentation and Deep Learning Techniques

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Several research articles related to this exist, like [8], which focus on biomedical signals techniques for the detection of Covid-19, [9], which centered on monitoring systems remotely for personal health during Covid-19 pandemic using biomedical signals, an overview of the diagnosis of Schizophrenia using a medical image by [10], recent advances, challenges, and way forward of medical image analysis utilizing machine learning techniques by [11], generative Adversarial Networks for Biomedical Image Analysis by [12,13] biomedical applications using machine learning techniques. All these articles focus on medical image processing or biomedical signals.…”

Section: Introductionmentioning

confidence: 99%

Computational Intelligence Approaches for Enhancing Biomedical Image Processing Applications Based on Breast Cancer

Isa¹,

Iliyas²,

Zarma³

2024

Biomedical Engineering

View full text Add to dashboard Cite

Recent advances in the cutting-edge technologies of biomedical sensing and image processing tools provide us with big data of biomedical and various types of images that can’t be processed within a finite period by professional clinicians. Various techniques for processing biomedical images comprise mathematical algorithms that extract vital diagnostic features from biomedical information and biological data. Because of the complexity and big size of the data computation, intelligence techniques have been applied in processing, visualizing, diagnostic, and classification tasks. This study will explore the effectiveness of the variously artificial intelligence approaches on biomedical signal and image processing applications. The researchers and community entirely will benefit from this study as a guide to the state-of-the-art artificial intelligence techniques for biomedical signal and image processing applications.

show abstract

Multibiosignal chaotic encryption scheme based on spread spectrum and global diffusion process for e-health

Murillo-Escobar

Cruz-Hernández

Cardoza-Avendaño

et al. 2022

Biomedical Signal Processing and Control

View full text Add to dashboard Cite

A comprehensive survey on the biomedical signal processing methods for the detection of COVID-19

Cited by 5 publications

References 71 publications

Detection of COVID-19 from Deep Breathing Sounds Using Sound Spectrum with Image Augmentation and Deep Learning Techniques

Detection of COVID-19 from Deep Breathing Sounds Using Sound Spectrum with Image Augmentation and Deep Learning Techniques

Computational Intelligence Approaches for Enhancing Biomedical Image Processing Applications Based on Breast Cancer

Multibiosignal chaotic encryption scheme based on spread spectrum and global diffusion process for e-health

Contact Info

Product

Resources

About