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.

用心跳间隔基础分类及深度学习模型，可以在只利用 60 秒心跳间隔的条件下显着地优于基于传统心率变异性预测模型，此模型基于稳定易感人群和紧急科室急性心肌梗死症状患者的 474 名患者的 ECGs，以及 1172 个患者。