Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks

Code

• github.com/lxdv/ecg-classification
  pytorch★ 307
• github.com/physhik/ecg-mit-bih
  tf★ 257
• github.com/xiaoxianedwindu/ecg-mit-bih-ae
  tf★ 0
• github.com/varocarras/ECG-523
  tf★ 0
• github.com/vkduy19/paper_implementations-master
  tf★ 0
• github.com/brain-bzh/health_exg
  none★ 0
• github.com/VinGPan/paper_implementations
  tf★ 0

Abstract

We develop an algorithm which exceeds the performance of board certified cardiologists in detecting a wide range of heart arrhythmias from electrocardiograms recorded with a single-lead wearable monitor. We build a dataset with more than 500 times the number of unique patients than previously studied corpora. On this dataset, we train a 34-layer convolutional neural network which maps a sequence of ECG samples to a sequence of rhythm classes. Committees of board-certified cardiologists annotate a gold standard test set on which we compare the performance of our model to that of 6 other individual cardiologists. We exceed the average cardiologist performance in both recall (sensitivity) and precision (positive predictive value).

Tasks

Reproductions