A heartbeat classification approach combining morphological and dynamic features with an SVM classifier achieved 99.3% accuracy in class-oriented evaluation and 86.4% in subject-oriented evaluation.
In this paper, we propose a new approach for heartbeat classification based on a combination of morphological and dynamic features. Wavelet transform and independent component analysis (ICA) are applied separately to each heartbeat to extract morphological features. In addition, RR interval information is computed to provide dynamic features. These two different types of features are concatenated and a support vector machine classifier is utilized for the classification of heartbeats into one of 16 classes. The procedure is independently applied to the data from two ECG leads and the two decisions are fused for the final classification decision. The proposed method is validated on the baseline MIT-BIH arrhythmia database and it yields an overall accuracy (i.e., the percentage of heartbeats correctly classified) of 99.3% (99.7% with 2.4% rejection) in the "class-oriented" evaluation and an accuracy of 86.4% in the "subject-oriented" evaluation, comparable to the state-of-the-art results for automatic heartbeat classification.
Ye et al. (Wed,) conducted a other in Arrhythmia. Heartbeat classification using morphological and dynamic features with SVM vs. State-of-the-art automatic heartbeat classification methods was evaluated on Overall accuracy of heartbeat classification into 16 classes. A heartbeat classification approach combining morphological and dynamic features with an SVM classifier achieved 99.3% accuracy in class-oriented evaluation and 86.4% in subject-oriented evaluation.