Artificial intelligence cloud platform improves arrhythmia detection from insertable cardiac monitors to 25 cardiac rhythm patterns through multi-label classification

• Published in: Journal of electrocardiology 2023-11, Vol.81, p.4-12
• Main Authors: Quartieri, Fabio, Marina-Breysse, Manuel, Toribio-Fernandez, Raquel, Lizcano, Carlos, Pollastrelli, Annalisa, Paini, Isabella, Cruz, Roberto, Grammatico, Andrea, Lillo-Castellano, José María
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Atrial Fibrillation - diagnosis; Bradycardia; Cloud Computing; Electrocardiography; Electrocardiography, Ambulatory; Humans; Insertable cardiac monitors; Retrospective Studies; Subcutaneous electrocardiograms
• ISSN: 0022-0736; 1532-8430
• DOI: 10.1016/j.jelectrocard.2023.07.001

Electrocardiogram (ECG) is the gold standard for the diagnosis of cardiac arrhythmias and other heart diseases. Insertable cardiac monitors (ICMs) have been developed to continuously monitor cardiac activity over long periods of time and to detect 4 cardiac patterns (atrial tachyarrhythmias, ventricular tachycardia, bradycardia, and pause). However, interpretation of ECG or ICM subcutaneous ECG (sECG) is time-consuming for clinicians. Artificial intelligence (AI) classifies ECG and sECG with high accuracy in short times. To demonstrate whether an AI algorithm can expand ICM arrhythmia recognition from 4 to many cardiac patterns. We performed an exploratory retrospective study with sECG raw data coming from 20 patients wearing a Confirm Rx™ (Abbott, Sylmar, USA) ICM. The sECG data were recorded in standard conditions and then analyzed by AI (Willem™, IDOVEN, Madrid, Spain) and cardiologists, in parallel. In nineteen patients, ICMs recorded 2261 sECGs in an average follow-up of 23 months. Within these 2261 sECG episodes, AI identified 7882 events and classified them according to 25 different cardiac rhythm patterns with a pondered global accuracy of 88%. Global positive predictive value, sensitivity, and F1-score were 86.77%, 83.89%, and 85.52% respectively. AI was especially sensitive for bradycardias, pauses, rS complexes, premature atrial contractions, and inverted T waves, reducing the median time spent to classify each sECG compared to cardiologists. AI can process sECG raw data coming from ICMs without previous training, extending the performance of these devices and saving cardiologists' time in reviewing cardiac rhythm patterns detection. [Display omitted]