ECG signal monitoring based on Covid-19 patients: Overview

Saddik, Rachid Latif Amine; Bella, Abdoullah

doi:10.54216/jisiot.020202

Cited by 3 publications

(2 citation statements)

References 12 publications

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“…First, a filter is used to remove unwanted spikes from the signal. Then, normalize the signal and apply a high pass and a low pass filter to cut off low and high frequencies corresponding to 0.7 and 3 Hz [33]. These frequencies are generally caused by noise and sudden light change.…”

Section: Signal Filteringmentioning

confidence: 99%

Video-based Heart Rate Estimation using Embedded Architectures

Boussaki¹,

Latif²,

Saddik³

2023

IJACSA

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Monitoring a driver's heart rate is an important determinant to his health condition. The monitoring system must be accurate and non restrictive to the user's actions. Estimating the driver's change in his usual heart beat pattern can prevent undesirable outcomes. Several methods exist to estimate heart rate without any contact. In this paper, we are focusing on a method that uses remote photoplethysmography (rPPG). rPPG is a technique where heart rate is extracted from a PPG signal. The signal is extracted from the changes in blood flow that corresponds to the color variations recorded through an RGB camera. In this work, a different study that was based on an existing algorithm is presented to determine its processing time. The algorithm we proposed was divided into different global blocks and each block into different functional blocks (FBs). Though evaluating all the blocks' processing time, it was possible to determine the most time consuming functional blocks. The results are implemented on different architectures: Desktop, Odroid XU4 and Jetson Nano to provide a higher performance.

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Section: Signal Filteringmentioning

confidence: 99%

Video-based Heart Rate Estimation using Embedded Architectures

Boussaki¹,

Latif²,

Saddik³

2023

IJACSA

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“…In a small proportion of instances, the infection can quickly progress to pneumonia, respiratory arrest, and death. IL7, IL2, GCSF, IL10, MCP1, IP10, TNF, and MIP1A are all inflamed cytokine linked to that same progression [4].…”

Section: Introductionmentioning

confidence: 96%

Implementation of a quantum machine learning model for the categorization and analysis of COVID-19 cases

Kadry,

Samak,

Ghorashi

et al. 2023

IFS

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Coronavirus is a new pathogen that causes both the upper and lower respiratory systems. The global COVID-19 pandemic’s size, rate of transmission, and the number of deaths is all steadily rising. COVID-19 instances could be detected and analyzed using Computed Tomography scanning. For the identification of lung infection, chest CT imaging has the advantages of speedy detection, relatively inexpensive, and high sensitivity. Due to the obvious minimal information available and the complicated image features, COVID-19 identification is a difficult process. To address this problem, modified-Deformed Entropy (QDE) algorithm for CT image scanning is suggested. To enhance the number of training samples for effective testing and training, the suggested method utilizes QDE to generate CT images. The retrieved features are used to classify the results. Rapid innovations in quantum mechanics had prompted researchers to use Quantum Machine Learning (QML) to test strategies for improvement. Furthermore, the categorization of corona diagnosed, and non-diagnosed pictures is accomplished through Quanvolutional Neural Network (QNN). To determine the suggested techniques, the results are related with other methods. For processing the COVID-19 imagery, the study relates QNN with other existing methods. On comparing with other models, the suggested technique produced improved outcomes. Also, with created COVID-19 CT images, the suggested technique outperforms previous state-of-the-art image synthesis techniques, indicating possibilities for different machine learning techniques such as cognitive segmentation and classification. As a result of the improved model training/testing, the image classification results are more accurate.

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Real-time measurement and control system based on automation technology under the background of network environment

Chen

Zhang

2022

International Journal of Emerging Electric Power Systems

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The application of automation technology realizes the transformation of people’s production and life-style, and also promotes the transformation of the system form of the network environment. The application of automation in the current network industry is increasing, especially the use of advanced methods and technologies for automatic control. The purpose of this paper is to realize the research on the network measurement and control system in the field of automation technology to promote the ecological development of the network automation process. This paper proposes the use of neural network technology in automation technology to guide the construction of computer network measurement and control systems, which is helpful to analyze and solve the objective problems of system imbalance and improper command output in system automation. The automatic network control system is constructed and optimized by referring to the principles and laws of the neural networks, and the automation of the measurement and control system in the network field is realized. In the test of the system, the results show that when the experimental parameter setting is the input flow value of the flowmeter 2.05 sccm, the output frequency of the inverter in the system jumps between 25 and 40 Hz respectively. The specific parameters of the experiment set the output frequency of the inverter to 50 Hz, and the input flow value of the flowmeter jumps between 2.05 sccm and 4.00 sccm. Therefore, analyzing the measurement and control system of network automation is undoubtedly crucial to the current road in the field of network automation.

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ECG signal monitoring based on Covid-19 patients: Overview

Cited by 3 publications

References 12 publications

Video-based Heart Rate Estimation using Embedded Architectures

Video-based Heart Rate Estimation using Embedded Architectures

Implementation of a quantum machine learning model for the categorization and analysis of COVID-19 cases

Real-time measurement and control system based on automation technology under the background of network environment

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