Intelligent Patient Monitoring for Arrhythmia and Congestive Failure Patients Using Internet of Things and Convolutional Neural Network

Karboub, Kaouter; Tabaa, Mohamed; Dellagi, Sofiène; Abbas, Dandache; Moutaouakkil, Fouad

doi:10.1109/icm48031.2019.9021803

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…For example, in [ 45 , 47 , 55 , 68 , 70 , 80 , 85 , 98 , 103 , 107 , 108 , 112 ], the authors used smartwatches and smart wristbands to record vital signs. In addition, the authors in [ 40 , 44 , 49 , 63 , 64 , 76 , 78 , 83 , 91 , 93 , 96 , 102 , 110 , 120 , 121 ] used various wearable ECG devices such as smart vests and patches. In addition, the authors in [ 90 , 99 , 105 , 106 ] used smart belts to collect ECG data.…”

Section: Resultsmentioning

confidence: 99%

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Moshawrab

Adda

Bouzouane

et al. 2023

Sensors

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Background: The advancement of information and communication technologies and the growing power of artificial intelligence are successfully transforming a number of concepts that are important to our daily lives. Many sectors, including education, healthcare, industry, and others, are benefiting greatly from the use of such resources. The healthcare sector, for example, was an early adopter of smart wearables, which primarily serve as diagnostic tools. In this context, smart wearables have demonstrated their effectiveness in detecting and predicting cardiovascular diseases (CVDs), the leading cause of death worldwide. Objective: In this study, a systematic literature review of smart wearable applications for cardiovascular disease detection and prediction is presented. After conducting the required search, the documents that met the criteria were analyzed to extract key criteria such as the publication year, vital signs recorded, diseases studied, hardware used, smart models used, datasets used, and performance metrics. Methods: This study followed the PRISMA guidelines by searching IEEE, PubMed, and Scopus for publications published between 2010 and 2022. Once records were located, they were reviewed to determine which ones should be included in the analysis. Finally, the analysis was completed, and the relevant data were included in the review along with the relevant articles. Results: As a result of the comprehensive search procedures, 87 papers were deemed relevant for further review. In addition, the results are discussed to evaluate the development and use of smart wearable devices for cardiovascular disease management, and the results demonstrate the high efficiency of such wearable devices. Conclusions: The results clearly show that interest in this topic has increased. Although the results show that smart wearables are quite accurate in detecting, predicting, and even treating cardiovascular disease, further research is needed to improve their use.

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Section: Resultsmentioning

confidence: 99%

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Moshawrab

Adda

Bouzouane

et al. 2023

Sensors

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“…Wireless visual sensor networks (WVSNs) have recently emerged as a new type of sensor-based intelligent system, and their performance exceeds that of existing surveillance sensor networks. Furthermore, due to their small size and the dense spatial coverage that can be achieved, WVSNs can be flexibly deployed for various computer vision tasks, including patient monitoring [2], distributed multimedia-based surveillance systems [3], military, police, airport [4], border [5], urban and transport [6] applications, and other areas of public interest. WVSN surveillance systems have an extensive range of applications worldwide, but their implementation has remained a challenge, due to the need for connectivity between multiple sensors and their complicated setup.…”

Section: Introductionmentioning

confidence: 99%

Anomaly Based Camera Prioritization in Large Scale Surveillance Networks

Hussain¹,

Muhammad²,

Ullah³

et al. 2022

Computers, Materials &Amp; Continua

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Digital surveillance systems are ubiquitous and continuously generate massive amounts of data, and manual monitoring is required in order to recognise human activities in public areas. Intelligent surveillance systems that can automatically identify normal and abnormal activities are highly desirable, as these would allow for efficient monitoring by selecting only those camera feeds in which abnormal activities are occurring. This paper proposes an energy-efficient camera prioritisation framework that intelligently adjusts the priority of cameras in a vast surveillance network using feedback from the activity recognition system. The proposed system addresses the limitations of existing manual monitoring surveillance systems using a three-step framework. In the first step, the salient frames are selected from the online video stream using a frame differencing method. A lightweight 3D convolutional neural network (3DCNN) architecture is applied to extract spatio-temporal features from the salient frames in the second step. Finally, the probabilities predicted by the 3DCNN network and the metadata of the cameras are processed using a linear threshold gate sigmoid mechanism to control the priority of the camera. The proposed system performs well compared to state-of-theart violent activity recognition methods in terms of efficient camera prioritisation in large-scale surveillance networks. Comprehensive experiments and an evaluation of activity recognition and camera prioritisation showed that our approach achieved an accuracy of 98% with an F1-score of 0.97 on the Hockey Fight dataset, and an accuracy of 99% with an F1-score of 0.98 on the Violent Crowd dataset.

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Intelligent Patient Monitoring for Arrhythmia and Congestive Failure Patients Using Internet of Things and Convolutional Neural Network

Cited by 2 publications

References 16 publications

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

Anomaly Based Camera Prioritization in Large Scale Surveillance Networks

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