Table 2.

Summary table of reviewed machine learning classification methods, along with their objective, dataset, performance and validation. AF, atrial fibrillation; ANN, artificial neural network; CNN, convolutional neural network; HMM, hidden Markov model; LBBB, left bundle branch block; LD, linear discriminant; LSTM, long short-term memory network; MLP, multilayer perceptron; PVC, premature ventricular contraction; RBBB, right bundle branch block; RNN, recurrent neural network; SVEB, supraventricular ectopic beat; SVM, support vector machine; VEB, ventricular ectopic beat.

study	classes and focus	method	performance	validation
heartbeat classification
Chazal et al. [18]	normal, VEB, SVEB fusion of normal and VEB, unknown	LD with QRS-based and time intervals features	SVEB: 75.9% (sensitivity) VEB: 77.7% (sensitivity)	on 50 000 independent beats (MIT-BIH)
Llamedo & Martínez [4]	normal, VEB and SVEB classification	LD (RR intervals and wavelet transform features) with floating feature selection	93% global accuracy	on independent MIT-BIH beats and INCART
Yeh et al. [19]	normal, LBBB, RBBB, PVC, atrial premature contractions	LD	96.23% (global accuracy)	on 14 30-min excerpts (MIT-BIH)
Ubeyli [6]	normal, congestive heart failure, ventricular tachyarrhythmia, AF	SVM with error output correction code and discrete wavelet transform	98.61% accuracy	on 360 independent beats (Physionet)
Melgani & Bazi [20]	normal, atrial premature beat, PVC, RBBB, LBBB, and paced beat	SVM optimized by particle swarm optimization	89.72% accuracy	on 40 438 independent beats (from 20 patient records of MIT-BIH)
Asl et al. [21]	normal, PVC, AF, sick sinus syndrome, ventricular fibrillation, 2° heart block	SVM with heart rate variability features and discriminant analysis feature reduction	99.16% accuracy	on 463 testing segments of MIT-BIH (average over 100 different runs)
Nasiri et al. [22]	normal, RBBB, LBBB, and paced beat	SVM with principal component analysis and genetic algorithm	93.46% accuracy	50% of MIT-BIH for testing
Ganeshkumar & Kumaraswamy [23]	normal, PVC, paced, atrial premature beat, LBBB and RBBB	random forest (30 trees) on 150 beats from MIT-BIH	92.16% accuracy	not validated on independent dataset
de Oliveira et al. [24]	PVC detection	Bayesian network framework using channel fusion	99.69% sensitivity	on QT database (25%—23 765 beats for testing)
Coast et al. [25]	VEB detection (over American Heart Association database)	HMM with states corresponding to ECG waveforms or intervals	97.25% sensitivity	on 799 independent beats
Koski [26]	PVC detection	HMM and broken line approximation (30 states)	100% accuracy	on only 4 beats
Andreao et al. [27]	PVC detection	HMM and rule-based system	99.79% sensitivity	on 61 543 test beats from QT database
Niwas et al. [28]	normal, LBBB, RBBB, atrial premature beat, SVEB, PVC, AF, ventricular fibrillation, sick sinus syndrome, fusion of VEB and normal	ANN with heartbeat intervals and spectral entropy features	99.02% accuracy	on 180 (unspecified) independent datasets
Inan et al. [29]	PVC detection	feed-forward MLP with wavelet transform and time intervals features	96.82% accuracy	on 22 ECG recordings from MIT-BIH
Ubeyli [30]	normal, congestive heart failure, ventricular tachyarrhythmia, AF	RNN with Levenberg–Marquardt training algorithm and eigenvectors	98.06% accuracy	on 360 beats from Physionet
Lagerholm et al. [31]	normal, LBBB, RBBB, atrial premature, aberrated atrial premature, nodal premature, SVEB, VEB, fusion normal and VEB, ventricular flutter, atrial escape, nodal escape, ventricular escape, unknown	Self-organizing networks with Hermite transform and RR intervals features	1.5% of misclassification	not validated on independent database
Linh et al. [32]	normal, LBBB, RBBB, atrial premature, VEB, ventricular flatter wave, ventricular escape	TSK fuzzy network with Hermite transform	96% accuracy	on 3668 beats from MIT-BIH
Ozbay et al. [33]	normal, sinus bradycardia, ventricular tachycardia, sinus arrhythmia, atrial premature contraction, paced, RBBB, LBBB, AF and atrial flutter	MLP with fuzzy clustering neural network architecture	99.9% accuracy	on 5342 segments from 92 patients (MIT-BIH)
ECG recording analysis for patient diagnosis, monitoring and stratification
Mincholé et al. [34]	ischaemic and non-ischaemic ST-segment changes	multivariate discriminant analysis with Wilk's Lambda minimization	87.5% accuracy	cross validated estimation on LTST database
Faganeli & Jager [35]	patients with transient ischaemia episodes	decision trees with heart rate and Legendre polynomial coefficients features	98.1% sensitivity, 85.2% specificity	bootstrap method
Rahman et al. [8]	hypertrophic cardiomyopathy detection on 12-lead ECG	SVM and random forest with 264 time intervals and waveforms amplitude features	precision of 0.84	fivefold cross validation over 10 930 beats
Bailón et al. [36]	diagnosis of coronary artery disease	multivariate discriminant analysis with repolarization, depolarization and heart rate variability features	94% sensitivity, 92% specificity	cross validated estimation (leave one out)
Kawazoe et al. [37]	risk of ventricular fibrillation in Brugada syndrome patients	logistic regression with syncope, R–J interval, QRS duration, and Tpeak–Tend dispersion as features	97.1% sensitivity, 63.0% specificity	leave-one-out cross validation over 143 patients
Pourbabaee & Lucas [7]	paroxysmal AF episodes detection	MLP with time interval and morphological waveform features	87.5% accuracy	over 16 recordings from 2001 Computing in Cardiology challenge
Colloca et al. [38]	AF episodes detection	SVM optimized with grid-search	85.45% accuracy with 100% sensitivity to AF	Series 200 of the MIT-BIH Arrhythmia (with 8 AF subjects)
Asgari et al. [39]	AF episodes detection	SVM with stationary wavelet transform	97.0% sensitivity	twofold stratified cross validation on MIT-BIH
Acharya et al. [40]	ischaemic/dilated cardiomyopathy, complete heart block, sick sinus syndrome, AF, ectopics, normal	ANN with fuzzy equivalence	85–95% accuracy	on 66 testing samples
Zheng et al. [41]	congestive heart failure (from 2-lead ECGs)	CNN	94.7% accuracy	10-fold cross validation over 15 subjects
Kannathal et al. [9]	normal, abnormal (PVC, RBBB, LBBB, paced), life threatening (sick sinus syndrome, ischaemic heart disease, ventricular fibrillation)	ANN with radial basis function	99% accuracy	on 200 independent testing patients
Zhang et al. [12]	asystole, extreme bradycardia, extreme tachycardia, ventricular tachycardia or ventricular flutter/fibrillation arrhythmia	SVM with genetic algorithm	93% true positive rate	fivefold cross validation over 750 recordings (2015 Physionet Challenge)
real-time episodes' detection and wearable devices
Kiranyaz et al. [10]	VEB and SVEB detection	1D patient-specific CNN	98.6% accuracy	on 41 766 testing beats from MIT-BIH
Chauhan & Vig [42]	PVC, atrial premature contraction, paced, ventricular couplet	deep LSTM network	0.975 precision, 0.9645 F-score	testing set of unknown size
Jeon et al. [43]	normal beats, AF, myocardial ischaemia classification	SVM on ARM processor	95.1% sensitivity	10-fold cross validation over MIT-BIH AF, 2001 and 2004 CinC challenge and STT database
Leutheuser et al. [44]	normal and abnormal (all MIT-BIH labels that are not normal), on Android devices	decision tree classifier with heartbeat features	89.6% accuracy	not validated on independent dataset
Oresko et al. [11]	normal, RBBB, PVC, paced or fusion of paced and normal beat detection on smartphone	feed-forward MLP with QRS morphological beat pattern	99% accuracy for normal—81% accuracy for fusion	threefold cross validation over 5421 beats (MIT-BIH)
Oster et al. [45]	normal and SVEB, fusion and VEB, and unknown	switching Kalman filters with X-factor mode	99.2% F1-score	independent validation on INCART