Combination of rule based classification and decision trees to identify low quality ECG

ECG obtained from personal devices by untrained users need to be assessed for quality before they are sent to physicians through telemedicine services. This paper discusses a machine learning algorithm that identifies low quality ECG recordings to be used in these devices. The proposed algorithm uses a combination of rule based classification and decision trees. A set of 7 features describing physiological relationships between 12 ECG leads were used for machine learning. The algorithm was trained and tested using Physionet Computing in Cardiology Challenge 2011 test database using 5 fold cross validation. The technique of oversampling was used to reduce the effect of class imbalance in the database. The algorithm achieved a sensitivity of 91.2% and a specificity of 91.5% to differentiate low and high quality ECG recordings.