IoT-enabled cloud-based real-time remote ECG monitoring system

Sahu, Manju; Atulkar, Mithilesh; Ahirwal, Mitul Kumar; Ahamad, Afsar

doi:10.1080/03091902.2021.1921870

Cited by 30 publications

(9 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The effectiveness of Federated Learning methodologies in accurately predicting cardiovascular diseases is demonstrated by these studies, which also highlight the potential for customization of these methodologies. Frameworks for identifying cardiac diagnostic features and monitoring real-time remote electrocardiograms using the Internet of Things (IoT) in the healthcare industry have been proposed by Kumar et al [21] and Sahu et al [22]. Mishra et al [23] present a model that uses data from ECG and the Internet of Things to make predictions about heart disease.…”

Section: Iiliterature Reviewmentioning

confidence: 99%

Towards a Holistic Approach to Chronic Disease Management: Integrating Federated Learning and IoT for Personalized health Care

Dipika R. Birari

2024

jes

View full text Add to dashboard Cite

Chronic diseases, specifically Cardiovascular Disease (CVD), pose a substantial worldwide health obstacle, requiring innovative and comprehensive approaches to management. This study presents an innovative method for managing chronic diseases by combining Federated Learning (FL) and the Internet of Things (IoT). The goal is to offer tailored healthcare solutions. The study presents a new approach called Federated Transfer Learning (FTL) that incorporates Adaptive Gradient Clipping (AGC) to improve the performance of models and maintain privacy in a distributed network of healthcare devices. The primary objective of this study is to develop a holistic framework that seamlessly amalgamates data from various sources, including wearables, medical devices, and electronic health records, utilizing FL to build a centralized model while preserving data privacy at the individual level. The proposed Faster Than Light (FTL) algorithm with Automatic Gain Control (AGC) enhances the process of model convergence, guaranteeing stability and dependability in a federated learning environment. The research methodology entails implementing the suggested framework in the management of Cardiovascular Disease (CVD). The performance of the model is assessed using the Area Under the Curve (AUC) metric, resulting in an impressive AUC score of 92.4%. This highlights the efficacy of the integrated approach in precisely forecasting and controlling risks associated with cardiovascular diseases (CVD), showcasing its potential for widespread use in managing chronic diseases. The key findings emphasize the advantages of integrating IoT devices into the FL ecosystem, enabling the immediate surveillance and gathering of various health data. The decentralized nature of the learning process enables model training to occur without compromising data privacy, making it suitable for large-scale healthcare systems. Moreover, the implementation of Faster Than Light (FTL) with Automatic Gain Control (AGC) improves the resilience and effectiveness of the federated learning procedure, thereby enhancing the overall achievement of the suggested framework. In conclusion, this study presents a groundbreaking contribution to the field of chronic disease management, specifically targeting Cardiovascular Disease. The combination of Federated Learning and IoT, along with the innovative Federated Transfer Learning with Adaptive Gradient Clipping, not only achieves outstanding predictive accuracy but also guarantees the privacy and security of sensitive health data. The proposed framework holds significant promise for revolutionizing personalized healthcare solutions, paving the way for a more effective and patient-centric approach to chronic disease management.

show abstract

Section: Iiliterature Reviewmentioning

confidence: 99%

Towards a Holistic Approach to Chronic Disease Management: Integrating Federated Learning and IoT for Personalized health Care

Dipika R. Birari

2024

jes

View full text Add to dashboard Cite

show abstract

“…The authors in [21] offered a cloud-based method for remotely monitoring heart disease. To enable data visualization, fast reaction, and long-term connectivity between equipment and users, Hyper Text Transfer Protocol (HTTP) and Message Queuing Telemetry Transport (MQTT) servers had been employed.…”

Section: Related Workmentioning

confidence: 99%

Modelling of IoT-WSN Enabled ECG Monitoring System for Patient Queue Updation

Kaur¹,

Saini²,

Kaur³

2022

IJACSA

View full text Add to dashboard Cite

The advancement of communication technologies has led to the interconnection of different sensors using the Internet of Things (IoT) and Wireless Sensor Network (WSN). WSN for healthcare applications has expanded exponentially due to evolving advantages such as low power requirement of sensors, transmission accuracy, and cost-efficiency. For heart attack patients, the future lies in ECG monitoring in which wearable sensors can be used to acquire patient information. In this paper, an attempt has been made to develop a novel IoT-enabled WSN to record patient information for detection of heart attack and to update queue of patients to ensure prioritized medical attention to critical patients. In the WSN, the Rayleigh Fading channel has been used to transmit data that can be accessed using the cloud repository by the medical staff remotely. The distance from the patient to the medical staff is calculated using Euclidean distance. Further, SNR in comparison to throughput and BER has been computed. The higher SNR indicates the maximum information transfer from patient to hospital staff. The proposed system uses the Grasshopper Optimization and CBNN based disease classification system and bubble sort algorithm has been used for updating patient queue. The proposed GHOA and CBNN has shown improved accuracy of 2.14% over existing techniques like CNN which has accuracy around 82% for R-R feature selection of ECG signals as compared to 82.72% shown by GHOA-CBNN.

show abstract

“…erefore, the proposed study on victims with acute myocardial infarction PCI postoperative stage II remote home rehabilitation, compared with old style casualty rehabilitation efficacy [5], hope by the aid of remote ECG detection technology [6], real-time, and incessant intensive caring of the patient's health auxiliary PCI postoperative patients having acute myocardial infarction, the guidance of doctors treatment outside the hospital rehabilitation treatment and health management for a long time to make the rehabilitation management and follow-up of AMI patients precise and standardized [7], so as to improve patient compliance, further improve the continuity of AMI patients' diagnosis and treatment in and out of the hospital, reduce patients' rehospitalization rate and mortality, and improve the worth of life.…”

Section: Introductionmentioning

confidence: 99%

Efficacy of Phase II Remote Home Rehabilitation in Patients with Acute Myocardial Infarction after Percutaneous Coronary Intervention

Zhi

Zheng

et al. 2022

Contrast Media & Molecular Imaging

View full text Add to dashboard Cite

Objective. To assess the efficacy of home-based cardiac rehabilitation and traditional outpatient rehabilitation in stage II after percutaneous coronary intervention (PCI) in patients with acute myocardial infarction (AMI). Methods. From September 2019 to March 2020, 80 AMI patients in Cangzhou Central Hospital were randomly assigned to one of the two groups: the control group or the observation group, 40 cases in each group. The control group received old-fashioned outpatient rehabilitation treatment, and the study group received long-distance family rehabilitation nursing intervention. The blood pressure, examination results, compliance, satisfaction evaluation, incidence of cardiac events, heart rate, quality of life score, and 6-minute walking test were compared between the two groups. Results. There were no deaths in both groups. There were significant differences in heart failure, unstable angina pectoris, unplanned readmission rate, walking compliance, and 6-minute walking test at 6 months after discharge ( P < 0.05 ). There were substantial variances in left ventricular discharge portion, low-density lipoprotein, medication compliance, satisfaction, and quality of life ( P < 0.05 ); there was substantial inconsistency in the 6-minute walking test concerning the two groups afterwards discharge for 3 months ( P < 0.05 ). Conclusion. Home rehabilitation is a new home cardiac rehabilitation model with high efficiency, convenience, and whole process monitoring and barrier-free follow-up management. It can effectively improve the cardiac function, workout patience and worth of life expectancy of victims with AMI, improve their self-management awareness and rehabilitation compliance, reduce the risk of cardiac events, and have a positive impact on the prognosis and rehabilitation of patients with AMI.

show abstract

IoT-enabled cloud-based real-time remote ECG monitoring system

Cited by 30 publications

References 22 publications

Towards a Holistic Approach to Chronic Disease Management: Integrating Federated Learning and IoT for Personalized health Care

Towards a Holistic Approach to Chronic Disease Management: Integrating Federated Learning and IoT for Personalized health Care

Modelling of IoT-WSN Enabled ECG Monitoring System for Patient Queue Updation

Efficacy of Phase II Remote Home Rehabilitation in Patients with Acute Myocardial Infarction after Percutaneous Coronary Intervention

Contact Info

Product

Resources

About