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

arXiv: https://arxiv.org/abs/1711.03892 · HTML: https://mlsanity.com/paper/1711.03892/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2017.

## Built on by (most cited first, 3 of 3)

- [Electrocardiogram Classification and Visual Diagnosis of Atrial Fibrillation with DenseECG](https://mlsanity.com/paper/2101.07535/) (2021)
- [Atrial Fibrillation Detection Using Weight-Pruned, Log-Quantised Convolutional Neural Networks](https://mlsanity.com/paper/2206.07649/) (2022)
- [Towards Personalized Healthcare in Cardiac Population: The Development of a Wearable ECG Monitoring System, an ECG Lossy Compression Schema, and a ResNet-Based AF Detector](https://mlsanity.com/paper/2207.05138/) (2022)

## Abstract (arXiv)

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.