A novel method of diagnosing coronary heart disease by analysing ECG signals combined with motion activity

In this paper, we propose an effective method to automatically diagnose coronary heart disease by detecting ST segment episodes of ECG signals. To improve the diagnostic accuracy, we consider the motion activity of individual while monitoring ECG signals and we detect the motion activity of people through heart rate. Our method is based on clinical principle that ST segment depression is greater relative to heart rate (HR) in the recovery period compared with the exercise phase, which is stated in reference. Finally, the method is simulated by The Long-Term ST Database which has reference annotations about whether the person had coronary heart disease or not, with a diagnostic accuracy 80%.