Marta Carrara1, Luca Carozzi1, Travis J Moss2, Marco de Pasquale1, Sergio Cerutti1, Douglas E Lake2, J Randall Moorman2, Manuela Ferrario3. 1. Department of Electronics, Information and Bioengineering, Politecnico di Milano, P.zza Leonardo da Vinci 32, Milan, Italy. 2. Department of Medicine, University of Virginia, Box 800158, Charlottesville, VA, USA. 3. Department of Electronics, Information and Bioengineering, Politecnico di Milano, P.zza Leonardo da Vinci 32, Milan, Italy. Electronic address: manuela.ferrario@polimi.it.
Abstract
BACKGROUND: Identification of atrial fibrillation (AF) is a clinical imperative. Heartbeat interval time series are increasingly available from personal monitors, allowing new opportunity for AF diagnosis. GOAL: Previously, 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. METHODS: We tested algorithms on the NSR, AF and arrhythmia databases. RESULTS: When 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 24hour records showed good correlation of observed and predicted rhythms. CONCLUSION: Heart rate dynamical measures are effective ingredients in numerical algorithms to classify cardiac rhythm from the heartbeat intervals time series alone.
BACKGROUND: Identification of atrial fibrillation (AF) is a clinical imperative. Heartbeat interval time series are increasingly available from personal monitors, allowing new opportunity for AF diagnosis. GOAL: Previously, 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. METHODS: We tested algorithms on the NSR, AF and arrhythmia databases. RESULTS: When 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 24hour records showed good correlation of observed and predicted rhythms. CONCLUSION: Heart rate dynamical measures are effective ingredients in numerical algorithms to classify cardiac rhythm from the heartbeat intervals time series alone.
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