Densely Connected Convolutional Networks and Signal Quality Analysis to Detect Atrial Fibrillation Using Short Single-Lead ECG Recordings

TL;DR

This study develops an automatic ECG classification system using densely connected CNNs and signal quality analysis to detect atrial fibrillation and other rhythms from short single-lead recordings, aiming for scalable screening.

Contribution

It introduces a combined approach of signal quality index and dense CNN models for accurate rhythm classification from short ECG segments, advancing automated AF detection methods.

Findings

Achieved 0.80 F1 score on PhysioNet/CinC challenge test set.

CNN models effectively distinguish between NSR, AF, O, and noise.

Signal quality index improves classification reliability.

Abstract

The development of new technology such as wearables that record high-quality single channel ECG, provides an opportunity for ECG screening in a larger population, especially for atrial fibrillation screening. The main goal of this study is to develop an automatic classification algorithm for normal sinus rhythm (NSR), atrial fibrillation (AF), other rhythms (O), and noise from a single channel short ECG segment (9-60 seconds). For this purpose, signal quality index (SQI) along with dense convolutional neural networks was used. Two convolutional neural network (CNN) models (main model that accepts 15 seconds ECG and secondary model that processes 9 seconds shorter ECG) were trained using the training data set. If the recording is determined to be of low quality by SQI, it is immediately classified as noisy. Otherwise, it is transformed to a time-frequency representation and classified with the CNN as NSR, AF, O, or noise. At the final step, a feature-based post-processing algorithm classifies the rhythm as either NSR or O in case the CNN model's discrimination between the two is indeterminate. The best result achieved at the official phase of the PhysioNet/CinC challenge on the blind test set was 0.80 (F1 for NSR, AF, and O were 0.90, 0.80, and 0.70, respectively).