Feature extraction and wall motion classification of 2D stress echocardiography with relevance vector machines

Introduction of automated methods for heart function assessment have the potential to minimize the variance in operator assessment. This paper considers automated classification of rest and stress echocardiography. One previous attempt has been made to combine information from rest and stress sequences utilizing a Hidden Markov Model (HMM), which has proven to be the best performing approach to date [1]. Here, we propose a novel alternative feature selection approach using combined information from rest and stress sequences for motion classification of stress echocardiography, utilizing a Relevance Vector Machine (RVM) classifier. We describe how the proposed RVM method overcomes difficulties that occur with the existing HMM approach. Overall accuracy with the new method for global wall motion classification using datasets from 173 patients is 93.02%, showing that the proposed method outperforms the current state-of-the-art HMM-based approach (for which global classification accuracy is 84.17%).