Arrhythmia Classification from the Abductive Interpretation of Short Single-Lead ECG Records

TL;DR

This paper introduces a novel method for classifying arrhythmias in short single-lead ECGs using high-level features derived from abductive interpretation, combining global and sequence-based classifiers to improve accuracy.

Contribution

It presents a new approach that integrates morphological and rhythm features with machine learning classifiers, achieving top performance in a competitive challenge.

Findings

Achieved a final score of 0.83 on the Physionet/CinC dataset

Ranked first in the 2017 Physionet/CinC Challenge

Demonstrated effectiveness of abductive interpretation in ECG classification

Abstract

In this work we propose a new method for the rhythm classification of short single-lead ECG records, using a set of high-level and clinically meaningful features provided by the abductive interpretation of the records. These features include morphological and rhythm-related features that are used to build two classifiers: one that evaluates the record globally, using aggregated values for each feature; and another one that evaluates the record as a sequence, using a Recurrent Neural Network fed with the individual features for each detected heartbeat. The two classifiers are finally combined using the stacking technique, providing an answer by means of four target classes: Normal sinus rhythm, Atrial fibrillation, Other anomaly, and Noisy. The approach has been validated against the 2017 Physionet/CinC Challenge dataset, obtaining a final score of 0.83 and ranking first in the competition.