AF Classification from a Short Single Lead ECG Recording: the Physionet Computing in Cardiology Challenge 2017

Abstract

The PhysioNet/Computing in Cardiology (CinC) Challenge 2017 focused on differentiating AF from noise, normal or other rhythms in short term (from 9–61 s) ECG recordings performed by patients. A total of 12,186 ECGs were used: 8,528 in the public training set and 3,658 in the private hidden test set. Due to the high degree of inter-expert disagreement between a significant fraction of the expert labels we implemented a mid-competition bootstrap approach to expert relabeling of the data, levering the best performing Challenge entrants’ algorithms to identify contentious labels. A total of 75 independent teams entered the Challenge using a variety of traditional and novel methods, ranging from random forests to a deep learning approach applied to the raw data in the spectral domain. Four teams won the Challenge with an equal high F1 score (averaged across all classes) of 0.83, although the top 11 algorithms scored within 2% of this. A combination of 45 algorithms identified using LASSO achieved an F1 of 0.87, indicating that a voting approach can boost performance.

Detection of Atrial Fibrillation in ECG Hand-held Devices Using a Random Forest Classifier

“…The objective of the challenge is to classify each ECG recording into one of the following classes: healthy (normal), AF, other rhythms, and noisy. More detailed information can be found in [9]. The proposed feature extraction and classification approach will be presented next.…”

“…This work proposes a hybrid classification approach for ECGs recorded by the AliveCor hand-held devices [9]. It combines features from multi domains including time, frequency, time-frequency, phase space, and meta-level.…”

“…2) To design an effective classification algorithm in order to classify four ECG types including the AF rhythms The proposed algorithm is evaluated over recently released single-lead ECG dataset [9]. In Sections 3 and 4, the results and conclusion are discussed.…”

Can Supervised Learning Be Used to Classify Cardiac Rhythms?

“…The classification performance was evaluated for the training data set and the hidden test set of the competition [3]. The confusion matrix was build and the F 1 score is computed by…”

“…Based on these annotations derived from short ECG recordings, our goal is to classify the hidden test set provided by the PhysioNet/CinC Challenge 2017 [3]. The aim of the challenge is to assign the ECGs to one of these classes: normal sinus rhythm (N ), atrial fibrillation (A), an alternative rhythm (O), or too noisy to classify (∼).…”