IoT Platform for Real-Time Multichannel ECG Monitoring and Classification with Neural Networks

Granados, J. M. del Castillo; Westerlund, Tomi; Zheng, Lirong; Zou, Zhuo

doi:10.1007/978-3-319-94845-4_16

Cited by 15 publications

(12 citation statements)

References 8 publications

(6 reference statements)

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“…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%

“…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. They explored the use of a Cloud deep neural network architecture for the classification of ECG data into multiple cardiovascular conditions.…”

Section: Technology-aware Ecg Monitoring Systemsmentioning

confidence: 99%

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

Serhani

Kassabi

Ismail

et al. 2020

Sensors

215

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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

215

View full text Add to dashboard Cite

show abstract

“…Dimitra Azariadi et al proposed the cloud based ECG monitoring for diagnostic accuracy and cloud computing allows the analysis of data manageable in order to make improvements in personalized healthcare [18]. An algorithm for ECG analysis based on Discrete Wavelet Transform and classification based on Support Vector Machine for heartbeat diagnosis with highest accuracy and implemented it on an IoT-based embedded platform has been discussed by Zhe Yang et al [19].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Nearly all smart terminals with an internet browser can acquire ECG data conveniently, which has greatly alleviated the cross-platform issue. This idea has been mentioned by Jose Granados et al [19]. Internet of Things (IoT) platforms applied to health promise to supply solutions to the challenges in healthcare systems by providing tools for lowering costs while increasing efficiency in diagnostics and treatment [20].…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Neural Network Based Real Time Multi-Class Arrhythmia Classification in IoT-Cloud Platform

Ank

Niyas

2021

Preprint

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An arrhythmia is a condition which represents irregular beating of the heart, beating of the heart too fast, too slow, or too early compared to a normal heartbeat. Diagnosis of various cardiac conditions can be done by the proper analysis, detection, and classification of life-threatening arrhythmia. Computer aided automatic detection can provide accurate and fast results when compared with manual processing. This paper proposes a reliable and novel arrhythmia classification approach using deep learning. A Deep Neural Network (DNN) with three hidden layers has been developed for arrhythmia classification using MIT-BIH arrhythmia database. The network classifies the input ECG signals into six groups: normal heartbeat and five arrhythmia classes. The proposed model was found to be very promising with an accuracy of 99.45 percent. The real time signal classification and the application of internet of things (IOT) are the other highlights of the work.

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“…The objective of classification is to develop a classifier that learns the distribution of patterns in the set of labeled data. Classification has been used in numerous IoT use-cases including real-time ECG monitoring [40], twitter sentiment analysis [41], ebola virus outbreak control [42], real-time monitoring of breast cancer patients [43], automatic people counter in stores [44], real-time fall detection system for elderly people [45], defect detection in machines [46], cardiac arrest prediction [47], video surveillance [48], rice disease monitor and control [49], real-time condition monitoring of electric machines [50], etc.…”

Section: Data Mining Techniquesmentioning

confidence: 99%

Data Analytics in the Internet of Things: A Survey

Saleem

Chishti

2019

SCPE

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The plethora of sensors deployed in Internet of Things (IoT) environments generate unprecedented volumes of data, thereby creating a data deluge. Data collected from these sensors can be used to comprehend, examine and control intricate environments around us, facilitating greater intelligence, smarter decision-making, and better performance. The key challenge here is how to mine out proﬁcient information from such immense data. Copious solutions have been put forth to obtain valuable inferences and insights, however, these solutions are still in their developing stages. Moreover, conventional procedures do not address the surging analytical demands of IoT systems. Motivated to resolve this concern, this work investigates the key enablers for performing desired data analytics in IoT applications. A comprehensive survey on the identiﬁed key enablers including their role in IoT data analytics, use cases in which they have been applied and the corresponding IoT applications for the use cases is presented. Furthermore, open research challenges and future research opportunities are also discussed. This article can be used as a basis to foster advanced research in the arena of IoT data analytics.

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IoT Platform for Real-Time Multichannel ECG Monitoring and Classification with Neural Networks

Cited by 15 publications

References 8 publications

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

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

Deep Neural Network Based Real Time Multi-Class Arrhythmia Classification in IoT-Cloud Platform

Data Analytics in the Internet of Things: A Survey

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