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

• Published in: American journal of translational research Vol. 13; no. 10; pp. 11653 - 11661
• Main Authors: Jiang, Bin, Dong, Nengbin, Shou, Jinliang, Cao, Limei, Hu, Kai, Liu, Wencheng, Qi, Xia
• Format: Journal Article
• Language: English
• Published: e-Century Publishing Corporation 01.01.2021
• Subjects: Original
• ISSN: 1943-8141; 1943-8141

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).OBJECTIVETo 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).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.METHODSTwo 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.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).RESULTSCompared 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).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.CONCLUSIONThe 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.