Web-based remote monitoring of live EEG

Healy, Philip D.; O’Reilly, Ruairi; Boylan, Geraldine B.; Morrison, John P.

doi:10.1109/health.2010.5556574

Cited by 8 publications

(2 citation statements)

References 18 publications

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“…As another domain of application for RPM, [6] proposed an RPM system for monitoring the live electroencephalogram (EEG) feed in the critical care units. They used Adobe flash plugin to prepare a nearly-real-time updateable plot of the patients' EEG that was being transmitted through the worldwide-web and allow medical practitioners to review the results in a remote location.…”

Section: Related Workmentioning

confidence: 99%

A Flexible and Intelligent Framework for Remote Health Monitoring Dashboards

Fazeli,

Sarrafzadeh

2020

Preprint

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Developing and maintaining monitoring panels is undoubtedly the main task in the remote patient monitoring (RPM) systems. Due to the significant variations in desired functionalities, data sources, and objectives, designing an efficient dashboard that responds to the various needs in an RPM project is generally a cumbersome task to carry out. In this work, we present ViSierra, a framework for designing data monitoring dashboards in RPM projects. The abstractions and different components of this open-source project are explained, and examples are provided to support our claim concerning the effectiveness of this framework in preparing fast, efficient, and accurate monitoring platforms with minimal coding. These platforms will cover all the necessary aspects in a traditional RPM project and combine them with novel functionalities such as machine learning solutions and provide better data analysis instruments for the experts to track the information.

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Section: Related Workmentioning

confidence: 99%

A Flexible and Intelligent Framework for Remote Health Monitoring Dashboards

Fazeli,

Sarrafzadeh

2020

Preprint

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“…Traditionally, remote health monitoring using EEG signals was carried out by using telephonic lines based on copper wire networks [8]. However, this approach is not suitable for long distance transmission due to high telephonic charges, huge signal power loss and addition of various noises.…”

Section: Introductionmentioning

confidence: 99%

Secure EEG Signal Transmission for Remote Health Monitoring Using Optical Chaos

et al. 2019

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For the very first time, we present the use of optical chaos for the secure transmission of electroencephalogram (EEG) signals through optical fiber medium in remote health monitoring systems. In our proposed scheme, a semiconductor laser source is used to generate optical chaos, which hides EEG signal before its transmission over the optical fiber medium. The EEG signals are acquired by using a 14channel Emotiv headset device, which are then processed and rescaled to be compatible with the experimental environment (Optisystem). The mixing of EEG signals and chaos is performed using additive chaos masking scheme, which exhibits certain useful properties such as simplicity and easy recovery of the message. Chaotic data (a combination of EEG signals and chaos) is sent over the optical fiber medium to investigate propagation issues associated with secure EEG signal transmission. The scheme is implemented for long haul communication in which the linear impairments of optical fiber are controlled for successful transmission of the secure signal. The parameters at transmitting and receiving sides are selected to achieve synchronization, such that the transmitted signal could be subtracted from identical chaos to restore the original EEG signal at the receiving side. The scheme is tested for different lengths of the optical fiber cable in which the quality of the received signal is determined by obtaining Q-factors. This scheme could also be used with medical signals such as electrocardiography and electromyography.INDEX TERMS EEG signal, Emotiv, semiconductor laser, optical chaos, additive chaos masking, synchronization, remote health monitoring, long haul.

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