Automated J Wave Detection from Digital 12-lead Electrocardiogram

- Using different methods to ascertain early repolarization (ER) has resulted in a considerable degree of subjectivity in the diagnosis of ER, which now prohibits reasonable comparison of results from different studies. Especially challenging is the automatic detection of a J wave which typically presents itself as a slur or notch in the down stroke slope of R wave.
- The ability to automatically detect a J wave would help in developing a standardized, automated definition of this form of ER from the digital ECG, which could subsequently allow examining for a J wave in large databases that have digital ECG recording.
- In this report we report a method for automated detection of J wave from the resting 12-lead ECG by applying signal processing and functional data analysis techniques.

In this report we provide a method for automated detection of J wave, defined as a notch or slur in the descending slope of the terminal positive wave of the QRS complex, using signal processing and functional data analysis techniques. Two different sets of ECG tracings were selected from the EPICARE ECG core laboratory, Wake Forest School of Medicine, Winston Salem, NC. The first set was a training set comprised of 100 ECGs of which 50 ECGs had J-wave and the other 50 did not. The second set was a test set (n = 116 ECGs) in which the J-wave status (present/absent) was only known by the ECG Center staff. All ECGs were recorded using GE MAC 1200 (GE Marquette, Milwaukee, Wisconsin) at 10 mm/mV calibration, speed of 25 mm/s and 500 HZ sampling rate. All ECGs were initially inspected visually for technical errors and inadequate quality, and then automatically processed with the GE Marquette 12-SL program 2001 version (GE Marquette, Milwaukee, WI). We excluded ECG tracings with major abnormalities or rhythm disorder. Confirmation of the presence or absence of a J wave was done visually by the ECG Center staff and verified once again by three of the coauthors. There was no disagreement in the identification of the J wave state. The signal processing and functional data analysis techniques applied to the ECGs were conducted at Duke University and the University of Toronto. In the training set, the automated detection had sensitivity of 100% and specificity of 94%. For the test set, sensitivity was 89% and specificity was 86%. In conclusion, test results of the automated method we developed show a good J wave detection accuracy, suggesting possible utility of this approach for defining and detection of other complex ECG waveforms.