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

by   Tomás Teijeiro, et al.
Innovative Interfaces, Inc.

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.


page 1

page 2

page 3

page 4


Abductive reasoning as the basis to reproduce expert criteria in ECG Atrial Fibrillation identification

Objective: This work aims at providing a new method for the automatic de...

A Neural Network Approach to ECG Denoising

We propose an ECG denoising method based on a feed forward neural networ...

ECG Classification with a Convolutional Recurrent Neural Network

We developed a convolutional recurrent neural network to classify 12-lea...

Beat by Beat: Classifying Cardiac Arrhythmias with Recurrent Neural Networks

With tens of thousands of electrocardiogram (ECG) records processed by m...

Feature Extraction and Automated Classification of Heartbeats by Machine Learning

We present algorithms for the detection of a class of heart arrhythmias ...

Ranking in Genealogy: Search Results Fusion at Ancestry

Genealogy research is the study of family history using available resour...

Please sign up or login with your details

Forgot password? Click here to reset