Heart rhythm interpretation ECG using Artificial Intelligence
Research type
Research Study
Full title
Heart rhythm interpretation of far-field bipolar ECG leads from the upper-arm using deep machine learning algorithms and artificial intelligence methods.
IRAS ID
277763
Contact name
David McEneaney
Contact email
Sponsor organisation
Southern Health and Social Care Trust
Duration of Study in the UK
0 years, 2 months, 3 days
Research summary
Abnormal heart rhythms, or arrhythmias, provide important prognosis of cardiovascular disease and death in the UK. Due to an aging population, the number of cardiac patients requiring long-term ECG monitoring is increasing. However, long-term automatic heart rhythm analysis and arrhythmia detection techniques for personal wearable ECG devices, remains an outstanding challenge. The proposed project aims to use a clinical ECG knowledge base being development at the Craigavon-Area Hospital, SHSCT, to develop a robust artificial intelligence computational model for reliable real-time analysis of heart rhythms from novel arm ECG recordings, enabling the automatic interpretation of heart rhythms from body surface far-field bipolar ECG recordings on the left upper-arm; a comfortable location for long-term ECG monitoring, using previously developed arm-ECG wearable device (WAMECG; https://www.mdpi.com/2079-9292/8/11/1300/htm).
A clinical ECG database from in-hospital patients will be developed; it will include standard 12- lead ECG recordings and two novel bipolar upper-left arm-ECG leads.
People with heart disease or related conditions will benefit from this project’s deliverables; particularly, vulnerable elderly patients not fit for an implantable ECG monitor (loop-recorder). The early detection of life-threatening ventricular arrhythmias will facilitate risk stratification of cardiac patients, for their effective intervention.REC name
HSC REC B
REC reference
20/NI/0044
Date of REC Opinion
27 May 2020
REC opinion
Further Information Favourable Opinion