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A review of smart sensors coupled with Internet of Things and Artificial Intelligence approach for heart failure monitoring

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Abstract

Over the last decade, there has been a huge demand for health care technologies such as sensors-based prediction using digital health. With the continuous rise in the human population, these technologies showed to be potentially effective solutions to life-threatening diseases such as heart failure (HF). Besides being a potential for early death, HF has a significantly reduced quality of life (QoL). Heart failure has no cure. However, treatment can help you live a longer and more active life with fewer symptoms. Thus, it is essential to develop technological aid solutions allowing early diagnosis and consequently, effective treatment with possibly delayed mortality. Commonly, forecasts of HF are based on the generation of vast volumes of data usually collected from an individual patient by different components of the family history, physical examination, basic laboratory results, and other medical records. Though, these data are not effectively useful for predicting this failure, nevertheless, with the aid of advanced medical technology such as interconnected multi-sensory-based devices, and based on several medical history characteristics, the broad data provided machine learning algorithms to predict risk factors for heart disease of an individual is beneficial. There will be many challenges for the next decade of advancements in HF care: exploiting an increasingly growing repertoire of interconnected internal and external sensors for the benefit of patients and processing large, multimodal datasets with new Artificial Intelligence (AI) software. Various methods for predicting heart failure and, primarily the significance of invasive and non-invasive sensors along with different strategies for machine learning to predict heart failure are presented and summarized in the present study.

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Copyright © 2018, Elsevier)

Fig. 3

Copyright © 2016, Wolters Kluwer Health)

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Copyright © 2019, Hindawi) (CC BY 4.0)

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Copyright © 2020, Wiley Online Library) (CC BY 4.0)

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Abbreviations

ADC:

Analog to Digital Converter

AI:

Artificial Intelligence

ANN:

Artificial Neural Networks

Bp-NN:

Back propagation Neural Network

CNN:

Convolution Neural Network

CT:

Computed tomography (CT)

CVD:

Cardiovascular Disease

DT:

Decision Trees

EF:

Ejection Fraction

EG:

Electrocardiography

EHR:

Electronic Health Record

ESC:

European Society of Cardiology

GA:

Genetic Algorithms

HER:

Electronic Health Records

HF:

Heart Failure

HRV:

Heart Rate Variability

IBL:

Instance-Based Learning

IoT:

Internet of Things

K-NN:

K-Nearest Neighbours

LA:

Left Atrial

LV:

Left Ventricular

ML:

Machine Learning

NN:

Neural Networks

PCA:

Principal Component Analysis

PCA:

Principal Component Analysis

QoL:

Quality of Life

RF:

Random Forest

RL:

Rule

RNN:

Recurrent Neural Network

RV:

Right Ventricle

SVM:

Support Vector Machine

SVM:

Support Vector Machines

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Funding

This publication was supported by Qatar University Internal Grant No. IRCC-2020–013 and Sultan Qaboos University through Grant # CL/SQU-QU/ENG/20/01, respectively.

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Authors and Affiliations

  1. Center for Advanced Materials, Qatar University, P.O. Box 2713, Doha, Qatar

    Muni Raj Maurya, Najam U. S. Sahar Riyaz & Kishor Kumar Sadasivuni

  2. Department of Mechanical and Industrial Engineering, Qatar University, P.O. Box 2713, Doha, Qatar

    Muni Raj Maurya

  3. Center for Nanoscience and Technology, Institute of Science and Technology, Jawaharlal Nehru Technological University, Hyderabad, Telangana State, 500085, India

    M. Sai Bhargava Reddy

  4. Biomedical Research Center, Qatar University, P.O. Box 2713, Doha, Qatar

    Huseyin Cagatay Yalcin

  5. Mechanical and Industrial Engineering Department, College of Engineering, Sultan Qaboos University, Al-Khoudh, 123, PO-BOX 33, Muscat, Oman

    Hassen M. Ouakad & Issam Bahadur

  6. Department of Computer Engineering, Qatar University, P.O. Box 2713, Doha, Qatar

    Somaya Al-Maadeed

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  1. Muni Raj Maurya
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Correspondence to Hassen M. Ouakad or Kishor Kumar Sadasivuni.

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Muni Raj Maurya and Najam US Sahar Riyaz contributed equally to this work as first authors

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Maurya, M.R., Riyaz, N.U.S.S., Reddy, M.S.B. et al. A review of smart sensors coupled with Internet of Things and Artificial Intelligence approach for heart failure monitoring. Med Biol Eng Comput 59, 2185–2203 (2021). https://doi.org/10.1007/s11517-021-02447-2

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