Table A1.
Summary of the selected proposals.
| Ref. | Title | Year | Cites |
|---|---|---|---|
| [47] | Ultra-Low Power, Secure IoT Platform for Predicting Cardiovascular Diseases | 2017 | 66 |
| [40] | A deep learning approach for ECG-based heartbeat classification for arrhythmia detection | 2018 | 351 |
| [48] | Towards collaborative intelligent IoT eHealth: From device to fog, and cloud | 2020 | 77 |
| [49] | Noise Rejection for Wearable ECGs Using Modified Frequency Slice Wavelet 739 Transform and Convolutional Neural Networks | 2019 | 54 |
| [50] | An IoT patient monitoring based on fog computing and data mining: Cardiac arrhythmia usecase | 2020 | 59 |
| [51] | ECG signal processing and KNN classifier-based abnormality detection by VH-doctor for remote cardiac healthcare monitoring | 2020 | 23 |
| [52] | Designing Very Fast and Accurate Convolutional Neural Networks with Application in ICD and Smart Electrocardiograph Devices | 2023 | 1 |
| [53] | Improving R Peak Detection in ECG Signal Using Dynamic Mode Selected Energy and Adaptive Window Sizing Algorithm with Decision Tree Algorithm | 2021 | 5 |
| [54] | A Self-Contained STFT CNN for ECG Classification and Arrhythmia Detection at the Edge | 2022 | 8 |
| [55] | An Adaptive Cognitive Sensor Node for ECG Monitoring in the Internet of Medical Things | 2021 | 9 |
| [56] | One-Dimensional CNN Approach for ECG Arrhythmia Analysis in Fog-Cloud Environments | 2021 | 39 |
| [57] | Classification and analysis of cardiac arrhythmia based on incremental support vector regression on IOT platform | 2021 | 9 |
| [41] | IoT-based ECG monitoring for arrhythmia classification using Coyote Grey Wolf optimization-based deep learning CNN classifier | 2022 | 12 |
| [59] | Deep Cardiac Telemonitoring for Clinical Cloud Healthcare Applications | 2022 | - |
| [60] | An IoT enabled secured clinical health care framework for diagnosis of heart diseases | 2023 | - |
| [61] | Dew-based offline computing architecture for healthcare IoT | 2022 | 8 |
| [62] | A novel convolutional neural network structure for differential diagnosis of Wide QRS Complex Tachycardia | 2023 | - |
| [63] | Hybrid optimized convolutional neural network for efficient classification of ECG signals in healthcare monitoring | 2022 | 5 |
| [64] | KEdge: Fuzzy-Based Multi-AI Model Coalescence Solution for Mobile Healthcare System | 2023 | 1 |
| [65] | Prediction of heart abnormalities using deep learning model and wearabledevices in smart health homes | 2022 | 6 |
| [66] | Atrial Fibrillation Detection via Accelerometer and Gyroscope of a Smartphone | 2017 | 130 |
| [67] | Using PPG Signals and Wearable Devices for Atrial Fibrillation Screening | 2019 | 49 |
| [68] | Accurate detection of atrial fibrillation from 12-lead ECG using deep neural | 2020 | 71 |
| [69] | Identification of undiagnosed atrial fibrillation patients using a machine learning risk predicting algorithm and diagnostic testing (PULsE-AI): Study protocol for a randomised controlled trial | 2020 | 9 |
| [70] | Classification of Atrial Fibrillation and Acute Decompensated Heart Failure Using Smartphone Mechanocardiography: A Multi-label Learning Approach | 2020 | 16 |
| [71] | Hardware implementation of 1D-CNN architecture for ECG arrhythmia classification | 2023 | - |
| [67] | Classification of Aortic Stenosis Using Time–Frequency Features From Chest Cardio-Mechanical Signals | 2019 | 27 |
| [73] | Cardiac Output Estimation: Online Implementation for Left Ventricular Assist Device Support | 2020 | 4 |
| [74] | Revealing Unforeseen Diagnostic Image Features with Deep Learning by Detecting Cardiovascular Diseases From Apical 4-Chamber Ultrasounds | 2022 | - |
| [75] | A Wearable Sensor for Arterial Stiffness Monitoring Based on Machine Learning Algorithms | 2018 | 23 |
| [76] | Predicting cardiovascular events with deep learning approach in the context of the internet of things | 2021 | 24 |
| [77] | Bridging Nano and Body Area Networks: A Full Architecture for Cardiovascular Health Applications | 2022 | - |
| [78] | BDCaM: Big Data for Context-Aware Monitoring—A Personalized Knowledge Discovery Framework for Assisted Healthcare | 2016* | 145 |
| [79] | Non-invasive cuffless blood pressure and heart rate monitoring using impedance cardiography | 2022 | 2 |
| [80] | A Machine Learning-Empowered System for Long-Term Motion-Tolerant Wearable Monitoring of Blood Pressure and Heart Rate with Ear-ECG/PPG | 2017 | 85 |
| [81] | Toward Hypertension Prediction Based on PPG-Derived HRV Signals: a Feasibility Study | 2018 | 48 |
| [82] | Blind, Cuff-less, Calibration-Free and Continuous Blood Pressure Estimation using Optimized Inductuve Group Method of Data Handling | 2022 | 31 |
| [83] | Pervasive blood pressure monitoring using Photoplethysmogram (PPG) sensor | 2017 | 48 |
| [84] | Cuffless Continuous Blood Pressure Estimation From Pulse Morphology of Photoplethysmograms | 2019 | 30 |
| [85] | Continuous blood pressure measurement from one-channel electrocardiogram signal using deep-learning techniques | 2020 | 72 |
| [86] | Resource-Aware Mobile-Based Health Monitoring | 2016 | 25 |
| [87] | Smart assisted diagnosis solution with multi-sensor Holter | 2017 | 7 |
| [88] | Machine Learning and Mobile Health Monitoring Platforms: A Case Study on Research and Implementation Challenges | 2018 | 11 |
| [39] | HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments | 2020 | 432 |
| [89] | Utilizing IoT wearable medical device for heart disease prediction using higher order Boltzmann model: A classification approach | 2019 | 101 |
| [90] | Wireless high-frequency NLOS monitoring system for heart disease combined with hospital and home | 2020 | 13 |
| [91] | Construction and Application of a Medical-Grade Wireless Monitoring System for Physiological Signals at General Wards | 2020 | 22 |
| [98] | Ambient assisted living predictive model for cardiovascular disease prediction using supervised learning | 2021 | 23 |
| [43] | An IoT Framework for Heart Disease Prediction Based on MDCNN Classifier | 2020 | 188 |
| [41] | A novel three-tier Internet of Things architecture with machine learning algorithm for early detection of heart disease | 2018 | 258 |
| [92] | Adaptive Multi-Dimensional dual attentive DCNN for detecting Cardiac Morbidities using Fused ECG-PPG Signals | 2022 | - |
| [93] | Platform for Healthcare Promotion and Cardiovascular Disease Prevention | 2021 | 6 |
| [94] | MedAi: A Smartwatch-Based Application Framework for the Prediction of Common Diseases Using Machine Learning | 2023 | 1 |
| [95] | Detection of Cardiovascular Disease Based on PPG Signals Using Machine Learning with Cloud Computing | 2022 | 4 |
| [96] | A Predictive Analysis of Heart Rates Using Machine Learning Techniques | 2022 | 19 |
| [97] | An Open-Source Privacy-Preserving Large-Scale Mobile Framework for Cardiovascular Health Monitoring and Intervention Planning with an Urban African American Population of Young Adults: User-Centered Design Approach | 2022 | 2 |
| [98] | Ambient assisted living predictive model for cardiovascular disease prediction using supervised learning | 2021 | 23 |
| [99] | An Efcient AlexNet Deep Learning Architecture for Automatic Diagnosis of Cardio‑Vascular Diseases in Healthcare System | 2022 | 5 |
| [100] | Smart wearable model for predicting heart disease using machine learning | 2022 | 3 |
| [101] | Toward Real-Time, At-Home Patient Health Monitoring Using Reservoir Computing CMOS IC | 2021 | 2 |
| [102] | Portable and Real-Time IoT-Based Healthcare Monitoring System for Daily Medical Applications | 2022 | 3 |
| [103] | Dictionary Learning-Based Multichannel ECG Reconstruction Using Compressive Sensing | 2022 | 2 |
| [104] | Real-Time Cloud-Based Patient-Centric Monitoring Using Computational Health Systems | 2022 | 27 |
| [105] | A portable medical device for detecting diseases using Probabilistic Neural Network | 2022 | - |
| [106] | BeatClass: A Sustainable ECG Classification System in IoT-Based eHealth | 2021 | 34 |
| [107] | Non-contact Monitoring of Heart Rate Variability Using A Fiber Optic Sensor | 2023 | - |
| [108] | Remote Health Monitoring System for the Estimation of Blood Pressure, Heart Rate, and Blood Oxygen Saturation Level | 2023 | 1 |
| [109] | iKardo: An Intelligent ECG Device for Automatic Critical Beat Identification for Smart Healthcare | 2021 | 6 |
| [110] | Energy-Efficient Real-Time Heart Monitoring on Edge-Fog-Cloud Internet of Medical Things | 2021 | 15 |
| [111] | Real-Time Tele-Monitoring of Patients with Chronic Heart-Failure Using a Smartphone: Lessons Learned | 2016 | 58 |
| [112] | Fog based smart healthcare: a machine learning paradigms for IoT sector | 2022 | 2 |
| [113] | Real-Time Event-Driven Classification Technique for Early Detection and Prevention of Myocardial Infarction on Wearable Systems | 2018 | 75 |
| [114] | A High performance electronic nose system for the recognition of myocardial infarction and coronary artery diseases | 2021 | 24 |
| [115] | FETCH: A Deep Learning-Based Fog Computing and IoT Integrated Environment for Healthcare Monitoring and Diagnosis | 2022 | 32 |
| [116] | FedECG: A Federated Semi-supervised Learning Framework for Electrocardiogram Abnormalities Prediction | 2023 | - |
| [117] | Development of a PPG Sensor Array as a Wearable Device for Monitoring Cardiovascular Metrics | 2021 | 17 |
| [118] | AI-based stroke prediction system using body motion biosignals during walking | 2022 | 3 |
| [119] | A Machine Learning Pipeline for Measurement of Arterial Stiffness in A-Mode Ultrasound | 2021 | 2 |
| [120] | A Real-Time Tunable ECG Noise-Aware System for IoT-Enabled Devices | 2022 | 1 |
| [121] | Interpretable Rule Mining for Real-Time ECG Anomaly Detection in IoT Edge Sensors | 2023 | - |
| [129] | Proposition of novel classification approach and features for improved real-time arrhythmia monitoring | 2016 | 21 |
| [44] | A robust deep convolutional neural network with batch-weighted loss for heartbeat classification | 2019 | 158 |
| [38] | Arrhythmia detection using deep convolutional neural network with long duration ECG signals | 2018 | 579 |
| [42] | A new approach for arrhythmia classification using deep coded features and LSTM networks | 2019 | 256 |
| [130] | A Real-Time Arrhythmia Heartbeats Classification Algorithm Using Parallel Delta Modulations and Rotated Linear-Kernel Support Vector Machines | 2019 | 56 |
| [131] | Automated detection of shockable and non-shockable arrhythmia using novel wavelet-based ECG features | 2019 | 42 |
| [132] | Heart disease detection using hybrid of bacterial foraging and particle swarm optimization | 2020 | 23 |
| [133] | A Deep Biometric Recognition and Diagnosis Network with Residual Learning for Arrhythmia Screening Using Electrocardiogram Recordings | 2020 | 3 |
| [134] | Deep Multi-Scale Fusion Neural Network for Multi-Class Arrhythmia Detection | 2020 | 74 |
| [135] | Intelligent Health Vessel ABC-DE: An Electrocardiogram Cloud Computing Service | 2018 | 17 |
| [136] | Improving the safety of atrial fibrillation monitoring systems through human verification | 2017 | 11 |
| [137] | Validating the robustness of an internet of things based atrial fibrillation detection system | 2020 | 20 |
| [138] | Extracting deep features from short ECG signals for early atrial fibrillation detection | 2020 | 22 |
| [139] | Detection of Atrial Fibrillation in Compressively Sensed Electrocardiogram Measurements | 2020 | 15 |
| [140] | A Multi-tier Deep Learning Model for Arrhythmia Detection | 2020 | 115 |
| [141] | Arrhythmia classification from single-lead ECG signals using the inter-patient paradigm | 2021 | 50 |
| [142] | Time adaptive ECG driven cardiovascular disease detector | 2021 | 21 |
| [143] | ECG arrhythmia classification by using a recurrence plot and convolutional neural network | 2021 | 91 |
| [144] | Stacking segment-based CNN with SVM for recognition of atrial fibrillation from single-lead ECG recordings | 2021 | 33 |
| [145] | Exploring Deep Features and ECG Attributes to Detect Cardiac Rhythm Classes | 2021 | 91 |
| [146] | Automated ECG multi-class classification system based on combining deep learning features with HRV and ECG measures | 2022 | 17 |
| [147] | Deep learning-based multidimensional feature fusion for classification of ECG arrhythmia | 2021 | 15 |
| [148] | ECG signal classification to detect heart arrhythmia using ELM and CNN | 2022 | - |
| [149] | Multi-Lead ECG Classification via an Information-Based Attention Convolutional Neural Network | 2020 | 5 |
| [150] | Fuzz-ClustNet: Coupled fuzzy clustering and deep neural networks for Arrhythmia detection from ECG signals | 2023 | - |
| [151] | Inter-patient arrhythmia classification with improved deep residual convolutional neural network | 2022 | 21 |
| [152] | DeepArr: An investigative tool for arrhythmia detection using a contextual deep neural network from electrocardiograms (ECG) signals | 2023 | - |
| [153] | Two-stage detection method of supraventricular and ventricular ectopic beats based on sequential artificial features and heartbeats | 2023 | - |
| [154] | Automatic varied-length ECG classification using a lightweight DenseNet model | 2023 | - |
| [155] | Arrhythmia detection based on multi-scale fusion of hybrid deep models from single lead ECG recordings: A multicenter dataset study | 2022 | 2 |
| [151] | A deep learning approach to cardiovascular disease classification using empirical mode decomposition for ECG feature extraction | 2023 | 1 |
| [157] | Prediction of paroxysmal atrial fibrillation using new heart rate variability features | 2021 | 23 |
| [158] | Predicting Hypertensive Patients with Higher Risk of Developing Vascular Events Using Heart Rate Variability and Machine Learning | 2020 | 20 |
| [159] | Nonlinear Dynamic Modeling of Blood Pressure Waveform: Towards an Accurate Cuffless Monitoring System | 2020 | 22 |
| [160] | Predicting Systolic Blood Pressure in Real-Time Using Streaming Data and Deep Learning | 2021 | 15 |
| [161] | Cuffless blood pressure estimation based on composite neural network and graphics information | 2021 | 10 |
| [162] | PPG-based blood pressure estimation can benefit from scalable multi-scale fusion neural networks and multi-task learning | 2022 | 5 |
| [163] | A Refined Blood Pressure Estimation Model Based on Single Channel Photoplethysmography | 2022 | 4 |
| [164] | An advanced LAN model based on optimized feature algorithm: Towards hypertension interpretability | 2021 | 2 |
| [165] | Deep learning models for cuffless blood pressure monitoring from PPG signals using attention mechanism | 2021 | 51 |
| [166] | NABNet: A Nested Attention-guided BiConvLSTM network for a robust prediction of Blood Pressure components from reconstructed Arterial Blood Pressure waveforms using PPG and ECG signals | 2023 | 3 |
| [167] | DeepCNAP: A Deep Learning Approach for Continuous Noninvasive Arterial Blood Pressure Monitoring Using Photoplethysmography | 2022 | 5 |
| [168] | An IoT based efficient hybrid recommender system for cardiovascular disease | 2019 | 75 |
| [37] | Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques | 2019 | 888 |
| [169] | An efficient IoT based patient monitoring and heart disease prediction system using Deep learning modified neural network | 2020 | 92 |
| [170] | HDPM: An Effective Heart Disease Prediction Model for a Clinical Decision Support System | 2020 | 130 |
| [45] | A Healthcare Monitoring System for the Diagnosis of Heart Disease in the IoMT Cloud Environment Using MSSO-ANFIS | 2020 | 153 |
| [46] | Comprenhensive electrocardiographic diagnosis based on deep learning | 2020 | 146 |
| [171] | An Efficient IoT-Based Platform for Remote Real-Time Cardiac Activity Monitoring | 2020 | 35 |
| [172] | Multi-disease big data analysis using beetle swarm optimization and an adaptive neuro-fuzzy inference system | 2021 | 17 |
| [173] | Cardiac disease detection using cuckoo search enabled deep belief network | 2022 | 2 |
| [174] | Automatic diagnosis of cardiovascular disorders by sub images of the ECG signal using multi-feature extraction methods and randomized neural network | 2021 | 20 |
| [175] | An intelligent heart disease prediction system based on swarm artificial neural network | 2021 | 23 |
| [176] | A multi-label learning prediction model for heart failure in patients with atrial fibrillation based on expert knowledge of disease duration | 2023 | - |
| [177] | WoM-based deep BiLSTM: smart disease prediction model using WoM-based deep BiLSTM classifier | 2023 | - |
| [178] | A scalable and real-time system for disease prediction using big data processing | 2023 | 3 |
| [179] | A novel blockchain-enabled heart disease prediction mechanism using machine learning | 2022 | 15 |
| [180] | Heart rate estimation network from facial videos using spatiotemporal feature image | 2022 | 2 |
| [181] | Predictive analysis of cardiovascular disease using gradient boosting based learning and recursive feature elimination technique | 2022 | 3 |
| [182] | Effective heart disease prediction using novel MLP-EBMDA approach | 2022 | 22 |
| [183] | LightX3ECG: A Lightweight and eXplainable Deep Learning System for 3-lead Electrocardiogram Classification | 2023 | 2 |
| [184] | Heart Diseases Prediction based on Stacking Classifiers Model | 2023 | - |
| [185] | Tuning Multi-Layer Perceptron by Hybridized Arithmetic Optimization Algorithm for Healthcare 4.0 | 2022 | - |
| [186] | Optimized Signal Quality Assessment for Photoplethysmogram Signals Using Feature Selection | 2022 | 8 |
| [187] | Photoplethysmography-Based Heart Action Monitoring Using a Growing Multilayer Network | 2022 | - |
| [188] | KD-Informer: A Cuff-Less Continuous Blood Pressure Waveform Estimation Approach Based on Single Photoplethysmography | 2022 | 6 |
| [189] | Multiclass classification of myocardial infarction with convolutional and recurrent neural networks for portable ECG devices | 2018 | 98 |
| [190] | ST-Net: Synthetic ECG tracing for diagnosing various cardiovascular diseases | 2020 | 6 |
| [191] | Explainable Prediction of Acute Myocardial Infarction Using Machine Learning and Shapley Values | 2020 | 47 |
| [192] | Prognostic Value of Machine Learning in Patients with Acute Myocardial Infarction | 2022 | 8 |
| [193] | Efficient detection of myocardial infarction from single lead ECG signal | 2021 | 21 |
| [194] | Near real-time single-beat myocardial infarction detection from single-lead electrocardiogram using Long Short-Term Memory Neural Network | 2021 | 10 |
| [195] | Predicting cardiac disease from interactions of simultaneously-acquired hemodynamic and cardiac signals | 2021 | 8 |
| [196] | Multilayer perceptron based deep neural network for early detection of coronary heart disease | 2021 | 17 |
| [197] | Early Detection of Coronary Heart Disease using Ensemble Techniques | 2021 | 37 |
| [198] | Non-invasive detection of coronary artery disease from photoplethysmograph using lumped parameter modelling | 2022 | 2 |
| [199] | Combining Convolutional Neural Network and Distance Distribution Matrix for Identification of Congestive Heart Failure | 2018 | 36 |
| [200] | Deep Learning Electrocardiographic Analysis for Detection of Left-Sided Valvular Heart Disease | 2022 | 10 |
| [201] | High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy with Cardiovascular Deep Learning | 2022 | 32 |
| [202] | Convolutional neural network based automatic screening tool for cardiovascular diseases using different intervals of ECG signals | 2021 | 25 |
| [203] | Reaction-diffusion informed approach to determine myocardial ischemia using stochastic in-silico ECGs and CNNs | 2021 | 3 |
| [204] | A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy | 2021 | 19 |
| [205] | Identifying Stroke Indicators Using Rough Sets | 2020 | 18 |