Effectiveness of artificial intelligent cardiac remote monitoring system for evaluating asymptomatic myocardial ischemia in patients with coronary heart disease

Objective: To explore the effectiveness of cardiac remote monitoring system (CRMS) based on artificial intelligence-enabled ECG algorithm mode for evaluating asymptomatic myocardial ischemia (AMI) in patients with coronary artery disease (CAD). Methods: Two hundred CAD patients confirmed by coronary angiography (CA) in our hospital were included as the study subjects, 120 of whom developed myocardial ischemia (MI). All patients received 12-lead telephone remote ECG monitoring and evaluation. After monitoring, artificial intelligence-enabled ECG algorithm was performed to observe the detection rate of MI. Results: Compared with artificial intelligence enabled ECG algorithm combined with remote ECG monitoring system, the detection rate of remote ECG monitoring system in 120 MI patients was lower (96.67% vs. 86.67%, P<0.01). Among the 120 MI patients, there were 26 patients (21.67%) with symptomatic myocardial ischemia (SMI) and 94 patients (78.33%) with AMI. There was no difference between the two detection methods in the diagnosis of SMI (P>0.05), while there was a difference in the diagnosis of AMI (P<0.01). The degree and duration of ST segment decline and the threshold variability of MI were higher in SMI patients than those in AMI patients (P<0.001). It showed that the lowest frequency of MI was from 0:00 to 06:00, and the highest from 06:01 to 12:00, with significant difference compared with other time periods (P<0.05). Conclusion: The CRMS based on artificial intelligence-enabled ECG algorithm mode can significantly improve the detection rate of AMI. Moreover, small changes of ST segment in AMI patients and circadian rhythm of disease onset were presented.