A deep-learning classifier for cardiac arrhythmias

TL;DR

This paper introduces a deep learning approach that classifies cardiac arrhythmias from heart beat signals, using a compact neural network architecture that is both efficient and effective for real-time applications.

Contribution

The authors develop a novel neural network model with convolutional layers tailored to the problem's scale, achieving superior accuracy with fewer parameters compared to prior methods.

Findings

Achieves higher classification accuracy than previous models.

Uses a smaller, faster neural network suitable for IoT deployment.

Effectively localizes QRS complexes for improved classification.

Abstract

We report on a method that classifies heart beats according to a set of 13 classes, including cardiac arrhythmias. The method localises the QRS peak complex to define each heart beat and uses a neural network to infer the patterns characteristic of each heart beat class. The best performing neural network contains six one-dimensional convolutional layers and four dense layers, with the kernel sizes being multiples of the characteristic scale of the problem, thus resulting a computationally fast and physically motivated neural network. For the same number of heart beat classes, our method yields better results with a considerably smaller neural network than previously published methods, which renders our method competitive for deployment in an internet-of-things solution.