Online heart monitoring systems on the internet of health things environments: A survey, a reference model and an outlook

Santos, Marcus Alexandre G. dos; Muñoz, Roberto; Olivares, Rodrigo; Filho, Pedro Pedrosa Rebouças; Ser, Javier Del; Albuquerque, Victor Hugo C. de

doi:10.1016/j.inffus.2019.06.004

Cited by 113 publications

(51 citation statements)

References 58 publications

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“…With the growing attention on machine learning, the medical application of this technology has become a new focus [17]. Machine learning is an interdisciplinary subject, which involves probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory, and other disciplines [18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Three-Dimensional Speckle Tracking Echocardiography Parameters in Predicting Left Ventricular Remodeling

Zhong

Liu

et al. 2020

Journal of Healthcare Engineering

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Three-dimensional speckle tracking echocardiography (3D STE) is an emerging noninvasive method for predicting left ventricular remodeling (LVR) after acute myocardial infarction (AMI). Previous studies analyzed the predictive value of 3D STE with traditional models. However, no models that contain comprehensive risk factors were assessed, and there are limited data on the comparison of different 3D STE parameters. In this study, we sought to build a machine learning model for predicting LVR in AMI patients after effective percutaneous coronary intervention (PCI) that contains the majority of the clinical risk factors and compare 3D STE parameters values for LVR prediction. We enrolled 135 first-onset AMI patients (120 males, mean age 54 ± 9 years). All patients went through a 3D STE and a traditional transthoracic echocardiography 24 hours after reperfusion. A second echocardiography was repeated at the three-month follow-up to detect LVR (defined as a 20 percent increase in left ventricular end-diastolic volume). Six models were constructed using 15 risk factors. A receiver operator characteristic curve and four performance measurements were used as evaluation methods. Feature importance was used to compare 3D STE parameters. 26 patients (19.3%) had LVR. Our evaluation showed that RF can best predict LVR with the best AUC of 0.96. 3D GLS was the most valuable 3D STE parameters, followed by GCS, global area strain, and global radial strain (feature importance 0.146, 0.089, 0.087, and 0.069, respectively). To sum up, RF models can accurately predict the LVR after AMI, and 3D GLS was the best 3D STE parameters in predicting the LVR.

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Section: Introductionmentioning

confidence: 99%

A Comparison of Three-Dimensional Speckle Tracking Echocardiography Parameters in Predicting Left Ventricular Remodeling

Zhong

Liu

et al. 2020

Journal of Healthcare Engineering

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“…Formulae for measuring these terms are given below: [27]. More information is available in [28][29][30][31][32][33][34].…”

Section: Classification Accuracy Of the Classifiersmentioning

confidence: 99%

EEG signal based Modified Kohonen Neural Networks for Classification of Human Mental Emotions

Hemanth¹

2020

AIS

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“…Other studies provide a generic analysis of healthcare systems and relevant devices. In [ 11 ] the authors analyse the various prerequisites for successfully integrating medical systems in an IoT network. Other works focus on communication protocols used in IoMT systems.…”

Section: Introductionmentioning

confidence: 99%

Security in IoMT Communications: A Survey

Koutras

Stergiopoulos

Dasaklis

et al. 2020

Sensors

105

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The Internet of Medical Things (IoMT) couples IoT technologies with healthcare services in order to support real-time, remote patient monitoring and treatment. However, the interconnectivity of critical medical devices with other systems in various network layers creates new opportunities for remote adversaries. Since most of the communication protocols have not been specifically designed for the needs of connected medical devices, there is a need to classify the available IoT communication technologies in terms of security. In this paper we classify IoT communication protocols, with respect to their application in IoMT. Then we describe the main characteristics of IoT communication protocols used at the perception, network and application layer of medical devices. We examine the inherent security characteristics and limitations of IoMT-specific communication protocols. Based on realistic attacks we identify available mitigation controls that may be applied to secure IoMT communications, as well as existing research and implementation gaps.

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Online heart monitoring systems on the internet of health things environments: A survey, a reference model and an outlook

Cited by 113 publications

References 58 publications

A Comparison of Three-Dimensional Speckle Tracking Echocardiography Parameters in Predicting Left Ventricular Remodeling

A Comparison of Three-Dimensional Speckle Tracking Echocardiography Parameters in Predicting Left Ventricular Remodeling

EEG signal based Modified Kohonen Neural Networks for Classification of Human Mental Emotions

Security in IoMT Communications: A Survey

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