IoT &amp; Big Data in Smart Healthcare (ECG Monitoring)

Bansal, Malti; Gandhi, Bani

doi:10.1109/comitcon.2019.8862197

Cited by 46 publications

(28 citation statements)

References 37 publications

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“…Few of the researches specified additional supporting processes, such as data storage or encryption. Typically, IoT-based monitoring systems adopt most of the primary processes [12,13,71,72]. Other studies emphasized only a partial lifecycle that has to do with data acquisition, preprocessing, and processing for heartbeat detection or finding an annotation set depending on the purpose of the system [43][44][45]49,63,65].…”

Section: Ecg Monitoring Value Chain: Comparative Studymentioning

confidence: 99%

“…Numerous research initiatives developed smart devices for ECG monitoring [12,24,126,131,132], where these devices implemented intelligence either in the sensors themselves or within the network components. Examples of these researches include a remote patient monitoring system for cardiac care assistance using a self-configured sensor network, as developed in [131].…”

Section: Remote Ecg Monitoring Systemsmentioning

confidence: 99%

“…A great number of researches proposed relevant IoT-based solutions for ECG monitoring [12,27,132,[136][137][138][139][140][141][142]; these researches are centered around the use of IoT devices for real-time ECG acquisition, processing, and analytics. The authors in [27,136,137], and [138] proposed an IoT-based patient-continuous monitoring system using the ECG sensor.…”

Section: Technology-aware Ecg Monitoring Systemsmentioning

confidence: 99%

“…Other solutions, such as in [132], go beyond the adoption of traditional IoT-based ECG monitoring systems and further incorporate intelligent wireless sensors within a personal area network to handle data acquisition and limited processing, which helped in improving monitoring efficiency and urgent reactions in cases of emergency. Similarly, in [12], nanomaterial-enabled ECG sensors were used, which improved conductivity, electrical proprieties, and reduced the induced monitoring cost. Moreover, in [141], Granados et al proposed an IoT platform for real-time analysis and management of a network of bio-sensors and gateways.…”

Section: Technology-aware Ecg Monitoring Systemsmentioning

confidence: 99%

“…As a result, ECG monitoring systems have been developed and widely used in the healthcare sector for the past few decades and have significantly evolved over time due to the emergence of smart enabling technologies [10][11][12][13]. Nowadays, ECG monitoring systems are used in hospitals [14][15][16][17], homes [18][19][20], outpatient ambulatory settings [21][22][23], and in remote contexts [24].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Serhani

Kassabi

Ismail

et al. 2020

Sensors

231

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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: Ecg Monitoring Value Chain: Comparative Studymentioning

confidence: 99%

Section: Remote Ecg Monitoring Systemsmentioning

confidence: 99%

Section: Technology-aware Ecg Monitoring Systemsmentioning

confidence: 99%

Section: Technology-aware Ecg Monitoring Systemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Serhani

Kassabi

Ismail

et al. 2020

Sensors

231

View full text Add to dashboard Cite

show abstract

Internet of Things and Big Data Analytics for Smart Healthcare

Kumar

Prakash

Balaji

et al. 2022

Handbook of Intelligent Healthcare Analytics

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CARDPSoML: Comparative approach to analyze and predict cardiovascular disease based on medical report data and feature fusion approach

Sinha,

Narula,

Pandey

et al. 2024

Health Science Reports

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Background and AimsDiabetes patients are at high risk for cardiovascular disease (CVD), which makes early identification and prompt management essential. To diagnose CVD in diabetic patients, this work attempts to provide a feature‐fusion strategy employing supervised learning classifiers.MethodsPreprocessing patient data is part of the method, and it includes important characteristics connected to diabetes including insulin resistance and blood glucose levels. Principal component analysis and wavelet transformations are two examples of feature extraction techniques that are used to extract pertinent characteristics. The supervised learning classifiers, such as neural networks, decision trees, and support vector machines, are then trained and assessed using these characteristics.ResultsBased on the area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy, these classifiers' performance is closely evaluated. The assessment findings show that the classifiers have a good accuracy and area under the receiver operating characteristic curve value, suggesting that the suggested strategy may be useful in diagnosing CVD in patients with diabetes.ConclusionThe recommended method shows potential as a useful tool for developing clinical decision support systems and for the early detection of CVD in diabetes patients. To further improve diagnostic skills, future research projects may examine the use of bigger and more varied datasets as well as different machine learning approaches. Using an organized strategy is a crucial first step in tackling the serious problem of CVD in people with diabetes.

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IoT & Big Data in Smart Healthcare (ECG Monitoring)

Cited by 46 publications

References 37 publications

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

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

Internet of Things and Big Data Analytics for Smart Healthcare

CARDPSoML: Comparative approach to analyze and predict cardiovascular disease based on medical report data and feature fusion approach

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