1Bellos, Papadopoulos [30]2010To develop a system that estimates the severity of a health episode of patients by collecting and analyzing patient information for disease using wearable sensors.Fiducial features and ANN2Bond, Finlay [31]2010To design an approach to interpret the ECG recording of patients based on ECG Rule Markup Language (ecgRuleML) to externalize decision rules.XML knowledge-based3Minutolo, Sannino [28]2010To develop a mobile mHealth system to attend to abnormal or emergency for patients suffering from heart problemOntology4Catley, Smith [36]2010To develop a framework that can be used for supporting complex real-time ECG analysis based on clinical rules translation modelsData streaming5Mahmoodabadi, Ahmadian [37]2010To design an expert system for ECG heart arrhythmia detectionFiducial and Discrete Wavelet Transform6Karvounis, Katertsidis [38]2011To develop the Specialist’s Decision Support System (SDSS) for disease diagnosisSensorArt platform based ECG data analysis and knowledge extraction7Kopiec and Martyna [39]2011To use the SVM Machine Learning algorithm for ECG classification and detection of QRS-complexes, P- and T-waves in the 12-lead ECG signal.SVD, Haar wavelet, Discrete Fourier Transform and SVM8Abdullah, Zakaria [40]2011To predict the hypertension risk of patients using Fuzzy Expert System Fuzzy-based approach9Bellos, Papadopoulos [27]2011To design procedures for diagnosing and monitoring chronic heart disease patients remotelyMachine learning using SVM, Random Forests, Artificial Neural Networks, Decision Trees and Naïve Bayes10de Oliveira, Andreão [41]2011To develop a machine learning based statistical model for the detection of cardiac arrhythmiasBayesian Network11Acampora, Lee [42]2012To design an ECG-based decision support system for predicting cardiac quality levelOntology and Fuzzy-based approach12Cinaglia, Tradigo [43]2012The develop a mobile system for telecardiology investigationsRemote Radio-Consultation (RRC) system framework13Sahin, Tolun [44]2012To analyze various hybrid expert system approaches and their applicationsReview paper14Paliwal and Kiwelekar [45]2013To identify similarities and variabilities among existing MPMsBody Area Network (BAN) and Back-End Systems (BESys).15Belle, Kon [46]2013To survey applications and methodologies for designing computer-aided Decision
Support System in biomedical informatics Review paper16Lin, Labeau [47]2013a design of a telecommunication and computer-based system for monitoring patients in comorbid conditionLogical model-based algorithms17Prerana, Cheeran [48]2014To develop a mobile android application prototype that is compatible with existing ECG acquisition device for ECG analysisFiducial18Benharref, Serhani [9]2014To develop a smart and adaptive synchronization model for m-Health applicationsCost-oriented algorithms19Lin and Labeau [49]2014To design and implement a decision support system for monitoring patients with critical conditionsDWT and EMD methods for feature extraction and constraint logic programming model for diagnosis20Martínez-Pérez, de la Torre-Díez [50]2014To investigate existing mobile applications for medical decision supportReview paper21Sani, Islam [51]2014To develop a framework for remote diagnosis and monitoring of heart attack ambulatory patientRule-based diagnostic model22Tanantong, Nantajeewarawat [52]2014To develop a hybrid continuous cardiac monitoring framework for false alarm reduction Machine learning and rule-based ES23Thomas, Das [53]2014To develop a dual-tree complex wavelet transform based framework for ECG feature extractionFiducial and dual tree complex wavelet transform24Sterling, Huang [54]2015To develop a machine learning classification approach to analyze the electrocardiogram of atrial fibrillation patientsMP features and quadratic discriminant analysis-based classification25Alshraideh, Otoom [55]2015To apply data mining techniques for identifying individuals suffering from heart arrhythmias.Machine learning algorithms such as C 4.5, NN, SVM, Jrip and Naïve bayes26Amour, Hersi [25]2015To develop an ECG monitoring system to remotely monitor multiple patients with cardiovascular diseasesFiducial approach27Prakash [56]2015To develop an intelligent clinical decision support system for diagnosing heart diseaseCase-Based Reasoning28Cloughley, Bond [57]2016To construct an ECG interpretation of clinical decision support toolFiducial and SQL query29Desai, Martis [58]2016To investigate and evaluate the performance of selected feature extraction methods for ECG arrhythmia classificationDCT, DWT, PCA, ANOVA, and k-NN30Alickovic and Subasi [34]2016To design an automatic detection and classification model for arrhythmiaDWT and Random Forests classifier31Li, Wang [59]2016To detect attenuating frequencies of the ECG signal related to artifactsWavelet packet entropy and random forests32Ripoll, Wojdel [60]2016To develop an automatic screening method for predicting the need for ambulatory patient to require cardiology serviceDeep neural networks33Jeyalakshmi and Robin [61]2016To analyze Heart Rate Variability in the diagnosis of sleep apneaFuzzy-based34Kotevski, Koceska [62]2016To develop an e-health system for monitoring of vital physiological data of patientsOpen m-Health platform35Baheti [63]2016To present a guide for applying fuzzy logic approach in developing expert system for varieties of diseasesFuzzy-based methods36Hassan and Bhuiyan [64]2016To design a method for splitting of EEG signals into wavelet sub-bands based on spectral characteristicsTunable-Q factor wavelet transform and Random forest37Li, Wang [59]2016To develop a personalized automatic machine learning model for heartbeats classificationParallel general regression neural network38Hejazi, Al-Haddad [65]2016To develop ECG biometric authentication using kernel approach for ECG tracingNon-fiducial with Kernel-based39Gharehbaghi, Lindén, & Babic [66]2017To develop a machine learning model for developing decision support system for cardiac disease diagnosisHidden Markov model40Desai [67]2017To design an automated classification of normal and Coronary Artery Disease conditions of ECGDWT, DCT, PCA and k-NN, SVM41Domazet, Gusev [26]2017To provide design specifications for time-critical medical monitoring applicationsReal-time acquisition and processing using wearable biosensors42Thai, Minh [4]2017To develop an IoT mechanism for automatic extraction of information related to heart disease from filtered ECG signalsRevised Sequential Recursive algorithm, DWT and Fishers Linear Discriminant 43Yin and Jha [10]2017To present HDSS, a closed-loop multitier health decision support systemEnsemble approach44Hassan and Haque [68]2017To develop a wearable low-power sleep apnea monitoring device for in-home careTunable-Q factor wavelet transforms and RUSBoost classifier 45Zhang, Wang [69]2017To improve the classification performance of ECG diagnosis algorithmRecurrent neural networks (RNN) and density-based clustering technique46Hossain, Mirza [70]2017To minimize the response time and cost of operating cardiac emergency medical serviceCrowdsourcing method47Cairns, Bond [71]2017To improve the accuracy of interpretations of the 12-lead ECG and to minimize missed co-abnormalitiesDifferential Diagnosis Algorithm (DDA)48Sadrawi, Lin [35]2017to evaluate the performance of four datasets from PhysioNet physiological repositoryPeriodogram approach for VF detection49Krasteva, Jekova [72]2017To carry out a correlation analysis of 12-lead ECG signalsNon-fiducial using Cross-correlation method 50Jung and Lee [73]2017To design and evaluate ECG identification method based on non-fiducial feature extraction and window removal methodWindow removal method for feature extraction. Nearest neighbor (NN), support vector machine (SVM),
and linear discriminant analysis (LDA) for classification.51Hejazi, Al-Haddad [74]2017To develop ECG biometric authentication system based on non-fiducial autocorrelation methodNon-fiducial autocorrelation and kernel-based method and one-class SVM52Gahlot, Reddy [16]2018To review smart health monitoring approachesReview paper53Venkatesan, Karthigaikumar [18]2018To improve classification of arrhythmia detection systemDENLMS adaptive filter, Coiflet wavelet, HRV features and SVM54Wang, Sun [14]2018To analyze telemonitoring of health care based on intelligent analysis of unstructured big data in real-time.Big data55Yang, Si [17]2018To improve the ECG classification speed on a noisy ECG signal.PCANet and SVM56Jangra and Gupta [75]2018To develop real-time patient supervision system using IoT-based smart monitoring model.Internet-of-things framework57Pławiak [76]2018To create new and efficient methods for automated detection of myocardial dysfunctionsMachine learning using SVM, k-NN, PNN, and RBFNN58Kumar, Pachori [77]2018To develop an automatic approach for the diagnosis of AF patients entropy-based features in flexible analytic wavelet transform (FAWT)59Arteaga-Falconi, Al Osman [78]2018To develop a bimodal authentication system by fusing ECG and fingerprint featuresNon-fiducial using Morphological-based approach60Camara, Peris-Lopez [32]2018To develop a continuous authentication scheme based on ECG streams for real-time authenticationNon-fiducial using ECG streams method61Lee, Jeong [79]2018To design an effective method for fiducial points detection from ECG signalPolygonal approximation62Comito, Forestiero [80]2019To implement a set of services to support physicians in diagnosing or treating patients’ health issuesDeep learning63Chauhan, Vig [81]2019To automatically detect anomalous cardiac events directly from machine-readable, recorded ECG signalsMachine learning such as Multilayer perceptron, SVM and logistic
regression.64Goshvarpour and Goshvarpour [6]2019To develop an ECG-based automated human identification systemFiducial and non-fiducial based methods. Information gain ratio and k-NN.65Abdalla, Wu [33]2019To investigate on effective methods for arrhythmia detection and classificationNonlinear and nonstationary decomposition method66Jain and Kaur [82]2019To design a fuzzy expert system for the diagnosis of coronary artery heart disease. Fuzzy-based approach67Sharma, Madaan [83]2019To design an expert system to predict heart disease using Fuzzy approachFuzzy-based approach68Mincholé and Rodriguez [84]2019To apply deep learning algorithm for identification of normal and abnormal heart rhythmsDeep learning69Kaleem and Kokate [85]2019To develop an efficient, flexible filtering technique to remove noise from the heartbeat signalAdaptive Filtering and Artificial neural network70Jovic, Kukolja [86]2019To develop a MULtivariate TIme Series Analysis in the Biomedicine (MULTISAB) systemMultithread parallelization approach71Khatibi and Rabinezhadsadatmahaleh [87]2019To automatically classify ECG beats for arrhythmia detectionDeep learning, k-NN, SVM and Random forest72Li, White [21]2019To investigate and review the current state of mobile phone applications in cardiac arhythmology Review paper73Zarei and Asl [3]2020To introduce new features for the classification of sleep apnea and normal patientsMachine learning using GentleBoost classifier74Rong, Mendez [22]2020To study the new scientific applications of AI in biomedicineReview paper75Akhtar, Lee [88]2020To investigate state-of-the-art Big Data analytics toolsReview paper76Christo, Nehemiah [89]2020To apply optimization of tree-based classifier for heart disease diagnoses Co-operative Co-evolution and Random Forest77Subasi, Bandic [90]2020To develop intelligent cloud-based system with wearable biomedical sensors to predict chronic disorders in a real-timeMachine learning algorithms78Parekh, Shah [91]2020To give significant detection methods and systematic approaches to figure out the impacts and causes of fatigueMachine learning using ANN79Santra, Basu [92]2020To address the problem of redundancy and inconsistency in ECG knowledge discovery Rough set-based lattice structure80Kar, Sahu [93]2020To develop effective DSS for ECG signals analysis and arrhythmia detectionDual-tree complex wavelet transform81Bhatt, Dubey [94]2020To model a framework that can help avoid sudden cardiac arrest and sudden cardiac deathRisk factor identification method82 Fatma Murat, Ozal Yildirim [95]2020To carry out a systematic review of the state-of-the-art deep learning studies for heartbeats detection Deep learning83Raheja and Manoacha [96]2020To present an effective source for providing healthcare assistance with the help of global medical expertsMobile telecardiology-based Method84Maji, Mandal [97]2020To develop an intelligent healthcare monitoring systemIoT, fiducial points and machine learning 85Tseng, Wang [98]2021To develop a new deep learning framework for mobile ECG signal processing and interpretation Large kernel Convolutional neural network (LkNet)86Virgeniya and Ramaraj [99]2021To develop and evaluate a deep learning-based ECG recognition and classification modelDeep learning based Gated Recurrent
Unit (GRU) for feature extraction and Extreme Learning Machine (ELM) for interpretation87Zhang, Yang [100]2021To examine the interpretability of a deep learning model for ECG classification A deep convolutional Neural Network with SHapley Additive exPlanations method88Profti, Fall [101]2021To improve on the identification of drug induced Arrhythmia based on ECG analysisDeep learning Convolutional Neural Network89Cornely, Carrillo and Mirsky [102]2021To develop deep learning model for 12-, 6-, 4-, 3- and 2-lead ECG data during 2021 PhysioNet/Computing in Cardiology ChallengeKernel-based feature extraction using CNN and SqueezeNet deep network with transfer learning for interpretation90Jiang, Deng [103]2022To evaluate the performance of a deep learning model in detection of CRP levels from the ECG in patients with sinus rhythmCNN + fully connected layer (dense layer using Softmax)91Zhao, Huag [104]2022To construct a Deep learning model for rapid and effective detection of LVH using 12-lead ECGCNN + LSTM92Chang, Lin [105]2022To develop a deep learning model to predict the biological age of the heart based on ECG analysis of heart disorders2-layer Convolutional Neural Network93Mohotan, Motin [106]2022To develop an approach to overcome the large segment recordings limitation of Deep learning models for identification and classification of arrhythmic beats2D Convolutional Neural Network trained with Continuous Wavelength Transform (CWT) of ECG recordings 94Diamant, Di Achile [107]2022To predict impaired Heart Rate Recovery based on resting ECG waveform patternsDeep learning with CNN95Vaid, Johnson [108]2022To predict the presence of both LV and RV disease in a large
and ethnically diverse population.Fiducial and contextual details. Deep learning with 2-dimensional CNN96Liu, Liu [109]2022To develop a deep learning-enabled ECG interpretation model for automatically identify patients with Brugada syndrome (a rare variant of arrhythmia) at an early point in timeFiducial and Non-fiducial. Multilayer deep learning model based on transfer learning.