Artif Intell Med - Electrocardiogram analysis using a combination of statistical, geometric, and nonlinear heart rate variability features.


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JECTIVE: The paper addresses a common and recurring problem of electrocardiogram (ECG) classification based on heart rate variability (HRV) analysis. Current understanding of the limits of HRV analysis in diagnosing different cardiac conditions is not complete. Existing research suggests that a combination of carefully selected linear and nonlinear HRV features should significantly improve the accuracy for both binary and multiclass classification problems. The primary goal of this work is to evaluate a proposed combination of HRV features. Other explored objectives are the comparison of different machine learning algorithms in the HRV analysis and the inspection of the most suitable period T between two consecutively analyzed R-R intervals for nonlinear features.METHODS AND MATERIAL: We extracted 11 features from 5min of R-R interval recordings: SDNN, RMSSD, pNN20, HRV triangular index (HTI), spatial filling index (SFI), correlation dimension, central tendency measure (CTM), and four approximate entropy features (ApEn1-ApEn4). Analyzed heart conditions included normal heart rhythm, arrhythmia (any), supraventricular arrhythmia, and congestive heart failure. One hundred patient records from six online databases were analyzed, 25 for each condition. Feature vectors were extracted by a platform designed for this purpose, named ECG Chaos Extractor. The vectors were then analyzed by seven clustering and classification algorithms in the Weka system: K-means, expectation maximization (EM), C4.5 decision tree, Bayesian network, artificial neural network (ANN), support vector machines (SVM) and random forest (RF). Four-class and two-class (normal vs. abnormal) classification was performed. Relevance of particular features was evaluated using 1-Rule and C4.5 decision tree in the cases of individual features classification and classification with features' pairs.RESULTS: Average total classification accuracy obtained for top three classification methods in the two classes' case was: RF 99.7%, ANN 99.1%, SVM 98.9%. In the four classes' case the best results were: RF 99.6%, Bayesian network 99.4%, SVM 98.4%. The best overall method was RF. C4.5 decision tree was successful in the construction of useful classification rules for the two classes' case. EM and K-means showed comparable clustering results: around 50% for the four classes' case and around 75% for the two classes' case. HTI, pNN20, RMSSD, ApEn3, ApEn4 and SFI were shown to be the most relevant features. HTI in particular appears in most of the top-ranked pairs of features and is the best analyzed feature. The choice of the period T for nonlinear features was shown to be arbitrary. However, a combination of five different periods significantly improved classification accuracy, from 70% for a single period up to 99% for five periods.CONCLUSIONS: Analysis shows that the proposed combination of 11 linear and nonlinear HRV features gives high classification accuracy when nonlinear features are extracted for five periods. The features' combination was thoroughly analyzed using several machine learning algorithms. In particular, RF algorithm proved to be highly efficient and accurate in both binary and multiclass classification of HRV records. Interpretable and useful rules were obtained with C4.5 decision tree. Further work in this area should elucidate which features should be extracted for the best classification results for specific types of cardiac disorders.

Resumo Limpo

jectiv paper address common recur problem electrocardiogram ecg classif base heart rate variabl hrv analysi current understand limit hrv analysi diagnos differ cardiac condit complet exist research suggest combin care select linear nonlinear hrv featur signific improv accuraci binari multiclass classif problem primari goal work evalu propos combin hrv featur explor object comparison differ machin learn algorithm hrv analysi inspect suitabl period t two consecut analyz rr interv nonlinear featuresmethod materi extract featur min rr interv record sdnn rmssd pnn hrv triangular index hti spatial fill index sfi correl dimens central tendenc measur ctm four approxim entropi featur apenapen analyz heart condit includ normal heart rhythm arrhythmia supraventricular arrhythmia congest heart failur one hundr patient record six onlin databas analyz condit featur vector extract platform design purpos name ecg chao extractor vector analyz seven cluster classif algorithm weka system kmean expect maxim em c decis tree bayesian network artifici neural network ann support vector machin svm random forest rf fourclass twoclass normal vs abnorm classif perform relev particular featur evalu use rule c decis tree case individu featur classif classif featur pairsresult averag total classif accuraci obtain top three classif method two class case rf ann svm four class case best result rf bayesian network svm best overal method rf c decis tree success construct use classif rule two class case em kmean show compar cluster result around four class case around two class case hti pnn rmssd apen apen sfi shown relev featur hti particular appear toprank pair featur best analyz featur choic period t nonlinear featur shown arbitrari howev combin five differ period signific improv classif accuraci singl period five periodsconclus analysi show propos combin linear nonlinear hrv featur give high classif accuraci nonlinear featur extract five period featur combin thorough analyz use sever machin learn algorithm particular rf algorithm prove high effici accur binari multiclass classif hrv record interpret use rule obtain c decis tree work area elucid featur extract best classif result specif type cardiac disord

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