Identification of exercise-induced ischemia using QRS slopes.

In this work we studied a computer-aided approach using QRS slopes as unconventional ECG features to identify the exercise-induced ischemia during exercise stress testing and demonstrated that the performance is comparable to the experts' manual analysis using standard criteria involving ST-segment depression. We evaluated the performance of our algorithm using a database including 927 patients undergoing exercise stress tests and simultaneously collecting the ECG recordings and SPECT results. High resolution 12-lead ECG recordings were collected continuously throughout the rest, exercise, and recovery phases. Patients in the database were classified into three categories of moderate/severe ischemia, mild ischemia, and normal according to the differences in sum of the individual segment scores for the rest and stress SPECT images. Philips DXL 16-lead diagnostic algorithm was run on all 10-s segments of 12-lead ECG recordings for each patient to acquire the representative beats, ECG fiducial points from the representative beats, and other ECG parameters. The QRS slopes were extracted for each lead from the averaged representative beats and the leads with highest classification power were selected. We employed linear discriminant analysis and measured the performance using 10-fold cross-validation. Comparable performance of this method to the conventional ST-segment analysis exhibits the classification power of QRS slopes as unconventional ECG parameters contributing to improved identification of exercise-induced ischemia.