Performance study of digital pacer spike detection as sampling rate changes

In this study, we first collected ECG data at 32 ksps and 500 sps from 51 patients with an implanted cardiac pacemaker. We also included 16 non paced ECGs with severe motion artifact and static interference. We then annotated the pacer pulses (locations and classifications) using special viewing software and divided the data into training and testing sets. We developed three digital pacemaker stimulus detection algorithms, one for each of three different sampling rates (500 sps, 8 ksps, and 32 ksps) by using similar detection concepts. After tuning them on the training sets at the corresponding sampling rates, we investigated their performances. Results showed that the detection algorithm based on the 12-lead 500-sps data stream has a good performance (sensitivity or Se=77.51%; positive productive value, or +P=90.32%). The 8-ksps detector has a better performance (Se=95.37; +P=99.77). The digital pacemaker detection algorithm for 32 ksps or higher data provided almost 100% true pacer spike detection (Se=99.51 and +P=100%). In summary, high speed sampling at 32 ksps can provide accurate detection of modern pacers and leads to a significant improvement in detection of pacemaker stimuli compared to the reduced sampling rates used previously.