Classification of cardiac rhythm using heart rate dynamical measures: validation in MIT–BIH databases

BackgroundIdentification of atrial fibrillation (AF) is a clinical imperative. Heartbeat interval time series are increasingly available from personal monitors, allowing new opportunity for AF diagnosis.GoalPreviously, we devised numerical algorithms for identification of normal sinus rhythm (NSR), AF, and SR with frequent ectopy using dynamical measures of heart rate. Here, we wished to validate them in the canonical MIT–BIH ECG databases.MethodsWe tested algorithms on the NSR, AF and arrhythmia databases.ResultsWhen the databases were combined, the positive predictive value of the new algorithms exceeded 95% for NSR and AF, and was 40% for SR with ectopy. Further, dynamical measures did not distinguish atrial from ventricular ectopy. Inspection of individual 24 hour records showed good correlation of observed and predicted rhythms.ConclusionHeart rate dynamical measures are effective ingredients in numerical algorithms to classify cardiac rhythm from the heartbeat intervals time series alone.