2016/12/29 by Tamas Madl, Madl, Tamas
Computer Science · Medicine · #Artificial Intelligence (cs.AI) #Cardiac electrophysiology and arrhythmias #ECG Monitoring and Analysis #FOS: Computer and information sciences #Heart Rate Variability and Autonomic Control #I.2 #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE) #cs.AI #cs.LG #cs.NE
paper · pdf · doi:10.48550/arxiv.1612.09205
6 pages in NIPS 2016 Workshop on Machine Learning for Health (ML4HC)
arxiv created 2016/12/29 · openalex publication_date 2016/12/29 · arxiv updated 2016/12/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Despite of the pain and limited accuracy of blood tests for early recognition of cardiovascular disease, they dominate risk screening and triage. On the other hand, heart rate variability is non-invasive and cheap, but not considered accurate enough for clinical practice. Here, we tackle heart beat interval based classification with deep learning. We introduce an end to end differentiable hybrid architecture, consisting of a layer of biological neuron models of cardiac dynamics (modified FitzHugh Nagumo neurons) and several layers of a standard feed-forward neural network. The proposed model is evaluated on ECGs from 474 stable at-risk (coronary artery disease) patients, and 1172 chest pain patients of an emergency department. We show that it can significantly outperform models based on traditional heart rate variability predictors, as well as approaching or in some cases outperforming clinical blood tests, based only on 60 seconds of inter-beat intervals.