Deep neural heart rate variability analysis

TL;DR

This paper presents a deep learning model combining biological neuron models and neural networks to classify heart rate variability from short ECG segments, outperforming traditional predictors and approaching blood test accuracy.

Contribution

It introduces a novel hybrid deep learning architecture integrating cardiac biological models with neural networks for improved heart rate variability analysis.

Findings

Significantly outperforms traditional HRV predictors.

Approaches or exceeds clinical blood test accuracy.

Effective on short 60-second ECG segments.

Abstract

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.