A multi-class ECG beat classifier based on a truncated Karhunen-Loève transform and Gaussian mixture model demonstrated excellent performance in classifying ECG signals into disease categories.
A novel multi-class ECG beat classifier using truncated KLT representation and Gaussian mixture models effectively classifies heartbeat abnormalities.
Automatic classification of electrocardiogram (ECG) signals is of Paramount importance in the detection of a wide range of heartbeat abnormalities as aid to improve the diagnostic achieved by cardiologists. In this paper an effective multi-class beat classifier, based on statistical identification of a minimum-complexity model, is proposed. The classifier is trained by extracting from the ECG signal a multivariate random vector by means of a truncated Karhunen-Loève transform (KLT) representation. The resulting statistical model is thus estimated using a robust and efficient Expectation Maximization (EM) algorithm to find the optimal parameters of a Gaussian mixture model. Based on the above statistical characterization a multi-class ECG classifier was derived. The experiments, conducted on the ECG signals from the MIT-BIH arrhythmia database, demonstrated the excellent performance of this technique to classify the ECG signals into different disease categories, with a reduced model complexity.
Biagetti et al. (Wed,) conducted a other in Heartbeat abnormalities / Arrhythmia. Multi-class ECG beat classifier based on truncated KLT representation was evaluated on Classification performance into different disease categories. A multi-class ECG beat classifier based on a truncated Karhunen-Loève transform and Gaussian mixture model demonstrated excellent performance in classifying ECG signals into disease categories.