4.4 Article

AI-CardioCare: Artificial Intelligence Based Device for Cardiac Health Monitoring

Related references

Note: Only part of the references are listed.
Article Medical Informatics

Development of IoT-based mhealth framework for various cases of heart disease patients

A. S. Albahri et al.

Summary: A new fault-tolerant mHealth framework based on Internet of things (IoT) is proposed in this study to address healthcare service provision issues during frequent failures in a telemedicine architecture. Two models are presented to support telehealth development for chronic heart disease patients in remote environments. These models help classify patients with CHD and select appropriate hospitals using mHealth, ultimately improving triage processes and providing timely services and treatment for CVD patients.

HEALTH AND TECHNOLOGY (2021)

Article Chemistry, Multidisciplinary

Heart Diseases Diagnose via Mobile Application

Mesut Guven et al.

Summary: This work presents a mobile application powered by artificial intelligence that can detect heart abnormalities with approximately 92% accuracy in a connectionless fashion. This surpasses the diagnostic accuracy of most experienced medical doctors and has the potential to revolutionize the traditional method of diagnosing heart diseases.

APPLIED SCIENCES-BASEL (2021)

Article Computer Science, Artificial Intelligence

ECG heartbeat arrhythmias classification: a comparison study between different types of spectrum representation and convolutional neural networks architectures

Ali Mohammad Alqudah et al.

Summary: This research compares the performance of different spectrogram representations and CNN architectures in arrhythmia classification, with MobileNet CNN architecture and the bispectrum representation achieving the highest overall accuracy, demonstrating that the spectrogram representations provide important information about heart performance.

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING (2021)

Article Computer Science, Information Systems

A Lightweight CNN Model for Detecting Respiratory Diseases From Lung Auscultation Sounds Using EMD-CWT-Based Hybrid Scalogram

Samiul Based Shuvo et al.

Summary: This study proposed a lightweight convolutional neural network architecture for classifying respiratory diseases using hybrid scalogram-based features of lung sounds. Experimental results show that the proposed scheme achieves high accuracy rates and outperforms other models in terms of classification accuracy.

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS (2021)

Article Mathematical & Computational Biology

Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning

Rohit Bharti et al.

Summary: This study discusses the prediction of heart disease using machine learning algorithms and deep learning, achieves promising results through validation and analysis of different algorithms, and ultimately obtains satisfactory results.

COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE (2021)

Review Chemistry, Multidisciplinary

Wearable sensors and devices for real-time cardiovascular disease monitoring

Jian Lin et al.

Summary: This article reviews the latest developments in the surveillance of various physiological signals of flexible sensors for cardiovascular diseases, covering monitoring signals and discussing different mechanisms. The focus is on monitoring pulse signals and flexible sensor technologies for electrocardiogram (ECG), phonocardiogram (PCG), seismocardiogram/ballistocardiogram (SCG/BCG), and apexcardiogram (ACG) signals. Proposed future research directions based on the current research landscape.

CELL REPORTS PHYSICAL SCIENCE (2021)

Article Computer Science, Information Systems

CardioXNet: A Novel Lightweight Deep Learning Framework for Cardiovascular Disease Classification Using Heart Sound Recordings

Samiul Based Shuvo et al.

Summary: The proposed CardioXNet model shows outstanding performance in automatic detection of five classes of cardiac auscultation, outperforming previous methods by achieving up to 99.60% accuracy, 99.56% precision, 99.52% recall and 99.68% F1-score on average. The model has been tested on multiple datasets and demonstrated high accuracy metrics, making it suitable for CVD screening in low resource setups using memory constraint mobile devices.

IEEE ACCESS (2021)

Article Computer Science, Artificial Intelligence

Machine learning-based classification of cardiac diseases from PCG recorded heart sounds

Anjali Yadav et al.

NEURAL COMPUTING & APPLICATIONS (2020)

Article Cardiac & Cardiovascular Systems

Heart Disease and Stroke Statistics-2020 Update: A Report From the American Heart Association

Salim S. Virani et al.

CIRCULATION (2020)

Review Cardiac & Cardiovascular Systems

Mobile health and cardiac rehabilitation in older adults

John Bostrom et al.

CLINICAL CARDIOLOGY (2020)

Article Computer Science, Artificial Intelligence

An efficient convolutional neural network for coronary heart disease prediction

Aniruddha Dutta et al.

EXPERT SYSTEMS WITH APPLICATIONS (2020)

Article Health Care Sciences & Services

Intelligent Diagnosis of Heart Murmurs in Children with Congenital Heart Disease

Jiaming Wang et al.

JOURNAL OF HEALTHCARE ENGINEERING (2020)

Article Computer Science, Hardware & Architecture

Support Vector Machines-based Heart Disease Diagnosis using Feature Subset, Wrapping Selection and Extraction Methods

Syed Muhammad Saqlain Shah et al.

COMPUTERS & ELECTRICAL ENGINEERING (2020)

Article Computer Science, Information Systems

The Use of Time-Frequency Moments as Inputs of LSTM Network for ECG Signal Classification

Grzegorz Klosowski et al.

ELECTRONICS (2020)

Article Computer Science, Interdisciplinary Applications

Automatic diagnosis of multiple cardiac diseases from PCG signals using convolutional neural network

Neeraj Baghel et al.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE (2020)

Article Computer Science, Interdisciplinary Applications

Machine learning algorithm for clustering of heart disease and chemoinformatics datasets

K. Balaji et al.

COMPUTERS & CHEMICAL ENGINEERING (2020)

Review Cardiac & Cardiovascular Systems

Global Burden of Cardiovascular Diseases and Risk Factors, 1990-2019 Update From the GBD 2019 Study

Gregory A. Roth et al.

JOURNAL OF THE AMERICAN COLLEGE OF CARDIOLOGY (2020)

Article Computer Science, Artificial Intelligence

A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion

Farman Ali et al.

INFORMATION FUSION (2020)

Article Mathematical & Computational Biology

Classification of heart sound short records using bispectrum analysis approach images and deep learning

Ali Mohammad Alqudah et al.

NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS (2020)

Article Computer Science, Information Systems

HDPM: An Effective Heart Disease Prediction Model for a Clinical Decision Support System

Norma Latif Fitriyani et al.

IEEE ACCESS (2020)

Article Computer Science, Information Systems

A Healthcare Monitoring System for the Diagnosis of Heart Disease in the IoMT Cloud Environment Using MSSO-ANFIS

Mohammad Ayoub Khan et al.

IEEE ACCESS (2020)

Article Health Care Sciences & Services

Exploring feature selection and classification methods for predicting heart disease

Robinson Spencer et al.

DIGITAL HEALTH (2020)

Article Chemistry, Multidisciplinary

Classification of Heart Sound Signal Using Multiple Features

Yaseen et al.

APPLIED SCIENCES-BASEL (2018)

Proceedings Paper Computer Science, Interdisciplinary Applications

A Wireless Early Prediction System of Cardiac Arrest through IoT

Yosuf ElSaadany et al.

2017 IEEE 41ST ANNUAL COMPUTER SOFTWARE AND APPLICATIONS CONFERENCE (COMPSAC), VOL 2 (2017)

Article Engineering, Biomedical

Signal processing of heart signals for the quantification of non-deterministic events

Veronique Millette et al.

BIOMEDICAL ENGINEERING ONLINE (2011)