Engaged with the heart – the EKG ring STEMI detector. (25th November 2020)

• Record Type: Journal Article
• Authors: Mehta, S; Fernandez, F; Villagran, C; Cardenas, G; Vieira, D; Frauenfelder, A; Quintero, S; Vijayan, Y; Merchant, S; Tamayo, C.J
• Is Part Of: European heart journal. Volume 41:(2020)Supplement 2
• Publication Date: 2020-11-25
• Subjects: Remote Patient Monitoring and Telemedicine; Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; 616.12005
• DOI: 10.1093/ehjci/ehaa946.3480
• Languages: English
• ISSNs: 0195-668X

Abstract

Abstract: Background: Our previous experience in telemedicine-centered STEMI management networks has shown delayed presentations as one of the most relevant factors in the reduction of Symptom-to-Baloon times. In order to further improve outcomes, we delved into the applications of mathematical vector algebra and engineering to incorporate an innovative Artificial Intelligence-guided Single Lead EKG methodology into a wearable ring to provide a self-administered alternative to reliable and expedite STEMI screening. Purpose: To provide preliminary results of the application of ultra-wearable technology in a ring for accurate STEMI detection. Methods: Our present work was done in two steps – 1) Applying mathematical vector algebra to construct an accurate and practical AI-guided Single Lead EKG algorithm for STEMI detection compatible with wearable devices, and 2) To engineer this algorithm into a wearable ring for quick and reliable STEMI detection. Throughout our first step, we provided a group of new lead waveforms (Vn') by positioning a single lead-capable wearable device into the chest positions Cn (C1, C2, ..., C6) while touching the second electrode with a right-hand finger in the same device, all of which corresponded to the difference in electric potential between Right Arm (RA) and the correspondent conventional precordial Vn chest position. By using vector algebra, we recognized Vn' as the sum of (-aVR + Vn). Vector mathematical analysis was performed for 5, 783 STEMI (50%) and 5, 784 Not-STEMI (50%) EKG from a proprietary dataset, obtaining their corresponding new Vn' precordial leads. Finally, the AI-guided STEMI detector model was trained with 10, 410 EKG records (90%) and tested with 1, 157 EKG records (10%). Performance metrics were calculated to determine best new Lead for STEMI detection. In the second step, we engineered this methodology into a wearable ring device. When a patient presents chest discomfort or oppression, the most common reaction is to move the hands towards the chest. By mimicking this behavior and having our EKG-capable ring technology on the right hand, we replicate our methodology by positioning said ring to chest positions Cn to register an EKG trace of new Vn' precordial leads and calculated performance metrics to evaluate the correlation with previous experiment. Results: Test results shows Lead V2' as the best overall lead in detecting STEMI with 91.2% Accuracy, 89.6% Sensibility, and 92.9% Specificity. These results were reproduced with both methodologies. Conclusions: Preliminary outcomes of the implementation of our innovative Single Lead EKG methodology into an ultra-portable ring yielded promising results. Prospective studies will be needed to further validate this neoteric methodology for STEMI detection, nevertheless, we envision the potential future applications of this technology in the clinical setting, particularly with swift screening and activation of remote STEMI management networks. Funding Acknowledgement: Type of funding source: None