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Spectral and Time Domain Parameters for the Classification of Atrial Fibrillation

Batista, D. ; Fred, A. L. N.

Spectral and Time Domain Parameters for the Classification of Atrial Fibrillation, Proc INSTICC International Conf. on Bio-inspired Systems and Signal Processing - Biosignals, Lisbon, Portugal, Vol. 1, pp. 329 - 337, January, 2015.

Digital Object Identifier: 10.5220/0005283403290337

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Abstract
Atrial fibrillation (AF) is the most common type of arrhythmia. This work presents a pattern analysis approach to automatically classify electrocardiographic (ECG) records as normal sinus rhythm or AF. Both spectral and time domain features were extracted and their discrimination capability was assessed individually and in combination. Spectral features were based on the wavelet decomposition of the signal and time parameters translated heart rate characteristics. The performance of three classifiers was evaluated: k-nearest neighbour (kNN), artificial neural network (ANN) and support vector machine (SVM). The MIT-BIH arrhythmia database was used for validation. The best results were obtained when a combination of spectral and time domain features was used. An overall accuracy of 99.08 % was achieved with the SVM classifier.