Statistical analysis of ST segments in ECG signals for detection of ischaemic episodes

This paper highlights a new method for the detection of ischaemic episodes using statistical features derived from ST segment deviations in electrocardiogram (ECG) signal. Firstly, ECG records are pre-processed for the removal of artifacts followed by the delineation process. Then region of interest (ROI) is defined for ST segment and isoelectric reference to compute the ST segment deviation. The mean thresholds for ST segment deviations are used to differentiate the ischaemic beats from normal beats in two stages. The window characterization algorithm is developed for filtration of spurious beats in ischaemic episodes. The ischaemic episode detection is made through the coefficient of variation (COV), kurtosis and form factor. A bell-shaped normal distribution graph is generated for normal and ischaemic ST segments. The results show average sensitivity (S e ) 97.71% and positive predictivity (+P) 96.89% for 90 records of the annotated European ST-T database (EDB) after validation. These results are significantly better than those of the available methods reported in the literature. The simplicity and automatic discarding of irrelevant beats makes this method feasible for use in clinical systems.