An IoT and Fog Computing-Based Monitoring System for Cardiovascular Patients with Automatic ECG Classification Using Deep Neural Networks

Rincon, J. A.; Guerra-Ojeda, Sol; Carrascosa, Carlos; Julián, Vicente

doi:10.3390/s20247353

Cited by 34 publications

(22 citation statements)

References 54 publications

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“…When an abnormal heart rhythm is detected, the device connected to the wireless network will send an alarm signal and detected abnormal data to the cloud data center, and the hospital will provide timely assistance after obtaining the data. The conceived scenario is shown in Figure 12 [79]. This study verifies the practicability of MobileNet and confirms that the deep learning algorithm can run effectively on small embedded devices.…”

Section: Lightweight Algorithmsupporting

confidence: 60%

“…ImageNet dataset is not much different from that of the VGG model, the number of parameters of MobileNet is much less than that of the VGG model, as shown in Table 4. The portable ECG detection equipment used in studies uses MobileNet as the backbone network, which can obtain the user's ECG in real time and has a high accuracy rate [79]. The system uses three-lead ECG monitoring equipment to collect ECG signals and uses the local system composed of raspberry pi and an Intel NCS2 coprocessor to embed algorithms for intelligent detection.…”

Section: Lightweight Algorithmmentioning

confidence: 99%

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Research Progress of ECG Monitoring Equipment and Algorithms Based on Polymer Materials

Zhang

Liu

2021

Micromachines

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Heart diseases such as myocardial ischemia (MI) are the main causes of human death. The prediction of MI and arrhythmia is an effective method for the early detection, diagnosis, and treatment of heart disease. For the rapid detection of arrhythmia and myocardial ischemia, the electrocardiogram (ECG) is widely used in clinical diagnosis, and its detection equipment and algorithm are constantly optimized. This paper introduces the current progress of portable ECG monitoring equipment, including the use of polymer material sensors and the use of deep learning algorithms. First, it introduces the latest portable ECG monitoring equipment and the polymer material sensor it uses and then focuses on reviewing the progress of detection algorithms. We mainly introduce the basic structure of existing deep learning methods and enumerate the internationally recognized ECG datasets. This paper outlines the deep learning algorithms used for ECG diagnosis, compares the prediction results of different classifiers, and summarizes two existing problems of ECG detection technology: imbalance of categories and high computational overhead. Finally, we put forward the development direction of using generative adversarial networks (GAN) to improve the quality of the ECG database and lightweight ECG diagnosis algorithm to adapt to portable ECG monitoring equipment.

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Section: Lightweight Algorithmsupporting

confidence: 60%

Section: Lightweight Algorithmmentioning

confidence: 99%

Research Progress of ECG Monitoring Equipment and Algorithms Based on Polymer Materials

Zhang

Liu

2021

Micromachines

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“…It would be a first step towards the IoMT (Internet of Medical Things) [ 45 ]. It is also important to address the topics of edge computing and fog computing [ 46 , 47 ] in order to improve latency in communications and to reduce bandwidth.…”

Section: Discussionmentioning

confidence: 99%

Smart Assistive Architecture for the Integration of IoT Devices, Robotic Systems, and Multimodal Interfaces in Healthcare Environments

Brunete

Gambao

Hernando

et al. 2021

Sensors

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This paper presents a new architecture that integrates Internet of Things (IoT) devices, service robots, and users in a smart assistive environment. A new intuitive and multimodal interaction system supporting people with disabilities and bedbound patients is presented. This interaction system allows the user to control service robots and devices inside the room in five different ways: touch control, eye control, gesture control, voice control, and augmented reality control. The interaction system is comprised of an assistive robotic arm holding a tablet PC. The robotic arm can place the tablet PC in front of the user. A demonstration of the developed technology, a prototype of a smart room equipped with home automation devices, and the robotic assistive arm are presented. The results obtained from the use of the various interfaces and technologies are presented in the article. The results include user preference with regard to eye-base control (performing clicks, and using winks or gaze) and the use of mobile phones over augmented reality glasses, among others.

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“…The system was evaluated via a use case of arrhythmia detection and the results showed that the system can achieve a high level of accuracy with low latency. In [24], the authors also presented a Fog-based health motoring system for cardiovascular diseases. The collected ECG signals were sent via LoRa to Fog-based LoRa gateways where Fog-AI having deep learning module for the detection of Atrial fibrillation and other heart rhythms.…”

Section: Related Workmentioning

confidence: 99%

“…integrate Cloud resources. The performance of our proposed approach is compared with two existing approaches, namely IoT-Cloud [45] and IoT-Fog [24]. The IoT-Cloud solution uses AWS to run the DL model for processing IoT-device generated ECG signals in Cloud, whereas the IoT-Fog prototype exploits a cluster of 3 Raspberry Pi devices having Intel Neural Compute Stick 2 (NCS 2) to conduct the same operation on the Fog.…”

Section: B Efficiency Of the Hybrid Fog-cloud Infrastructurementioning

confidence: 99%

One-Dimensional CNN Approach for ECG Arrhythmia Analysis in Fog-Cloud Environments

et al. 2021

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Cardiovascular diseases are considered the number one cause of death across the globe which can be primarily identified by the abnormal heart rhythms of the patients. By generating electrocardiogram (ECG) signals, wearable Internet of Things (IoT) devices can consistently track the patient's heart rhythms. Although Cloud-based approaches for ECG analysis can achieve some levels of accuracy, they still have some limitations, such as high latency. Conversely, the Fog computing infrastructure is more powerful than edge devices but less capable than Cloud computing for executing compositionally intensive data analytic software. The Fog infrastructure can consist of Fog-based gateways directly connected with the wearable devices to offer many advanced benefits, including low latency and high quality of services. To address these issues, a modular one-dimensional convolution neural network (1D-CNN) approach is proposed in this work. The inference module of the proposed approach is deployable over the Fog infrastructure for analysing the ECG signals and initiating the emergency countermeasures within a minimum delay, whereas its training module is executable on the computationally enriched Cloud data centers. The proposed approach achieves the F1-measure score ≈1 on the MIT-BIH Arrhythmia database when applying GridSearch algorithm with the cross-validation method. This approach has also been implemented on a single-board computer and Google Colab-based hybrid Fog-Cloud infrastructure and embodied to a remote patient monitoring system that shows 25% improvement in the overall response time.

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An IoT and Fog Computing-Based Monitoring System for Cardiovascular Patients with Automatic ECG Classification Using Deep Neural Networks

Cited by 34 publications

References 54 publications

Research Progress of ECG Monitoring Equipment and Algorithms Based on Polymer Materials

Research Progress of ECG Monitoring Equipment and Algorithms Based on Polymer Materials

Smart Assistive Architecture for the Integration of IoT Devices, Robotic Systems, and Multimodal Interfaces in Healthcare Environments

One-Dimensional CNN Approach for ECG Arrhythmia Analysis in Fog-Cloud Environments

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