Classification of ECG arrhythmias based on statistical and time-frequency features
Kadbi MH., Hashemi J., Mohseni HR., Maghsoudi A.
In this paper a new approach to accurately classify ECG arrhythmias through a combination of the wavelet transform and artificial neural network is presented. Three kinds of features in a very computationally efficient manner are computed as follows: 1-Joint time-frequency features (discrete wavelet transform coefficients). 2-Time domain features (R-R intervals). 3-Statistical feature (form factor). Using these features, the limitations of other methods in classifying multiple kinds of arrhythmia with high accuracy for all of them at once are overcome. Finally, a cascade classifier including two ANNs has been designed. Considering the whole MIT-BIH arrhythmia database, 10 kinds of arrhythmia were classified. The overall accuracy of classification of the proposed approach is above 90%.