A novel abnormal ECG beats detection method

Automatic detection of life threatening abnormal beats in electrocardiogram (ECG) signal is of importance in many healthcare applications. The ECG beat signal variations in both shape and time impose great challenges to automatic detection tasks. To address those challenges and for high accuracy automatic detection, we present here a two stage abnormal beats detection algorithm. Normal and abnormal beat types are represented by templates which are selected from training data using clustering. Multiple templates for each beat type well represent the distribution of the data and allow nonlinearity of the discrimination. Critical features are extracted for both templates and incoming beat signals in discrete wavelet transform domain. The template matching is carried out using time invariant dynamic time warping (DTW). For those the DTW distance cannot provide sharp discrimination, an additional stage of verification is invoked to further check among those types having small distance with the incoming beat, based on ECG wave's time interval features. The algorithm is tested on a large data set (23 records containing 28114 normal beats and 4633 abnormal beats) and achieves an accuracy of 97.24%.