Skip to main content
. 2022 Oct 14;9:1001982. doi: 10.3389/fcvm.2022.1001982

Figure 3.

Figure 3

Validation of 12-lead ECG signals between devices. The proposed deep learning model for STEMI detection was based on the digital 12-lead ECG signals recorded using a computerized ECG machine (GE Healthcare MAC 2000/3500/5500, USA). For the prehospital 12-lead ECG acquisition in the current study, we used a mini portable ECG device (QT Medical, Diamond Bar, CA, USA) with the proposed AI algorithm integrated within. To ensure the efficacy of AI-based STEMI detection using the mini-portable ECG device, we checked the consistency of the 12-lead ECG output signals between the two devices. A total of 194 verified ECGs acquired from GE machines (GE-ECGs) were converted into the corresponding QT Medical ECG output format (QT-ECGs) using a certified ECG simulator. Finally, the signal similarities between raw GE-ECGs and the transcribed QT-ECGs was analyzed. The performance of the AI model in classifying data as “STEMI” or “Not STEMI” for each of the two sets of ECG signals was compared to attest to the consistency of AI performance across devices.