Combining Low-dimensional Wavelet Features and Support Vector Machine for Arrhythmia Beat Classification.

ABSTRACT: Automatic feature extraction and classification are two main tasks in abnormal ECG beat recognition. Feature extraction is an important prerequisite prior to classification since it provides the classifier with input features, and the performance of classifier depends significantly on the quality of these features. This study develops an effective method to extract low-dimensional ECG beat feature vectors. It employs wavelet multi-resolution analysis to extract time-frequency domain features and then applies principle component analysis to reduce the dimension of the feature vector. In classification, 12-element feature vectors characterizing six types of beats are used as inputs for one-versus-one support vector machine, which is conducted in form of 10-fold cross validation with beat-bas

SUBMITTER: Qin Q 

PROVIDER: S-EPMC5519637 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature