Long-tail effect on ECG classification

Heart disease affects seriously to human health. ECG signal is critical information to help doctor with heart diagnose prediction. In previous studies on ECG classifier, state-of-art method use MIT dataset to evaluate prediction result and record a high accuracy. However, the dataset has a long tail phenomenon where the number of normal beats is cover 83,6% of all dataset whereas some diagnose beats have a few samples. Therefore, in this paper, the state-of-art method was used to evaluate the system performance where long tail effect is removed. This method was tested in two scenarios, the first scenario is that it considers normal beat as a class for recognition, therefore we could have long-tail effect in the result. The second only consider diagnose beats where long-tail effect is removed. The experiment proves that long-tail phenomenon could affect seriously to prediction result.