Utilising Fog Computing for Developing a Person-Centric Heart Monitoring System

Akrivopoulos, Orestis; Amaxilatis, Dimitrios; Mavrommati, Irene; Chatzigiannakis, Ioannis

doi:10.1109/ie.2018.00010

Cited by 8 publications

(9 citation statements)

References 12 publications

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“…Of particular interest are systems that propose solutions to optimize processing and reduce the cost of data transmission and storage over the Cloud infrastructure [24,26,71,[143][144][145], this is also very crucial in cases of emergency and vital situations when real-time analytics are urgently required for actionable insights. Tuli et al [26] incorporated ensemble deep learning in edge computing devices and deployed it for the real-life application of automatic heart disease analysis.…”

Section: Technology-aware Ecg Monitoring Systemsmentioning

confidence: 99%

“…Similarly, wearable devices were empowered with processing capabilities to locally (at the edge) analyze the signal and identify abnormal behaviors [144]. However, wearable embedded devices, mobile edge devices, and Cloud services were combined to provide reliable, accurate, and real-time heart monitoring [25,143]. Wearable devices are remotely trained to interpret heart abnormalities and the Fog extends the Cloud by migrating data-processing closer to the production site, thus accelerating the system's responsiveness to events.…”

Section: Technology-aware Ecg Monitoring Systemsmentioning

confidence: 99%

See 1 more Smart Citation

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

Serhani

Kassabi

Ismail

et al. 2020

Sensors

214

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Health monitoring and its related technologies is an attractive research area. The electrocardiogram (ECG) has always been a popular measurement scheme to assess and diagnose cardiovascular diseases (CVDs). The number of ECG monitoring systems in the literature is expanding exponentially. Hence, it is very hard for researchers and healthcare experts to choose, compare, and evaluate systems that serve their needs and fulfill the monitoring requirements. This accentuates the need for a verified reference guiding the design, classification, and analysis of ECG monitoring systems, serving both researchers and professionals in the field. In this paper, we propose a comprehensive, expert-verified taxonomy of ECG monitoring systems and conduct an extensive, systematic review of the literature. This provides evidence-based support for critically understanding ECG monitoring systems’ components, contexts, features, and challenges. Hence, a generic architectural model for ECG monitoring systems is proposed, an extensive analysis of ECG monitoring systems’ value chain is conducted, and a thorough review of the relevant literature, classified against the experts’ taxonomy, is presented, highlighting challenges and current trends. Finally, we identify key challenges and emphasize the importance of smart monitoring systems that leverage new technologies, including deep learning, artificial intelligence (AI), Big Data and Internet of Things (IoT), to provide efficient, cost-aware, and fully connected monitoring systems.

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Section: Technology-aware Ecg Monitoring Systemsmentioning

confidence: 99%

Section: Technology-aware Ecg Monitoring Systemsmentioning

confidence: 99%

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

Serhani

Kassabi

Ismail

et al. 2020

Sensors

214

View full text Add to dashboard Cite

show abstract

“…Attack detection is designed to analyze the network traffic or operating behaviors presented during network operation. We extract representative attack features and judge the security and stability of system operation [7]. Currently, network attacks against power monitoring systems mainly present composite attacks, and traditional single threat source detection methods are not sufficient to detect multiple types of composite attacks.…”

Section: Active Defense Algorithm For Compound Attacks On Power Monitoring Networkmentioning

confidence: 99%

“…We use the gradient descent method to optimize the parameter a, b of J(a, b). Then at G and x p = at p +b, we can get the deviation degree at t p according to Equation (7).…”

Section: Definitionmentioning

confidence: 99%

Safety Protection Technology of Power Monitoring System Based on Feature Extraction Algorithm

Xiangbei

Yuhong

Wenqian

et al. 2021

DGAEJ

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The network security protection technology of power monitoring systems is of great significance. Aiming at the power network monitoring and protection technology problem, the paper proposes an active monitoring and protection strategy based on a feature extraction algorithm. The algorithm can calculate the transfer degree of security incidents based on evidence theory. First, the paper obtains a specific state transition diagram based on the security topology of a generalized random power communication network. Then, we analyze the relationship between power system information security and engineering security based on the system’s operating results and feature extraction algorithms. The experimental results demonstrate the rapid effectiveness of this method.

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“…The goal of this paper is to develop new, light-weight algorithms for the analysis and interpretation of ECG sensor data that can be executed in the embedded processor provided by a wearable device. The motivation here is to transfer data processing and analysis tasks to the edges of the network so that we can increase the longevity of the wearable devices by reducing energy requirements through reduced transmissions of large sensor data [21,22]. In this way, the wearable device becomes capable of analyzing and interpreting sensor-data traces to provide actionable alerts without any dependence on cloud services.…”

Section: Introductionmentioning

confidence: 99%

Fog-Computing-Based Heartbeat Detection and Arrhythmia Classification Using Machine Learning

et al. 2019

Self Cite

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Designing advanced health monitoring systems is still an active research topic. Wearable and remote monitoring devices enable monitoring of physiological and clinical parameters (heart rate, respiration rate, temperature, etc.) and analysis using cloud-centric machine-learning applications and decision-support systems to predict critical clinical states. This paper moves from a totally cloud-centric concept to a more distributed one, by transferring sensor data processing and analysis tasks to the edges of the network. The resulting solution enables the analysis and interpretation of sensor-data traces within the wearable device to provide actionable alerts without any dependence on cloud services. In this paper, we use a supervised-learning approach to detect heartbeats and classify arrhythmias. The system uses a window-based feature definition that is suitable for execution within an asymmetric multicore embedded processor that provides a dedicated core for hardware assisted pattern matching. We evaluate the performance of the system in comparison with various existing approaches, in terms of achieved accuracy in the detection of abnormal events. The results show that the proposed embedded system achieves a high detection rate that in some cases matches the accuracy of the state-of-the-art algorithms executed in standard processors.

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Utilising Fog Computing for Developing a Person-Centric Heart Monitoring System

Cited by 8 publications

References 12 publications

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

Safety Protection Technology of Power Monitoring System Based on Feature Extraction Algorithm

Fog-Computing-Based Heartbeat Detection and Arrhythmia Classification Using Machine Learning

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