# Diagnosing Cardiac Abnormalities from 12-Lead Electrocardiograms Using Enhanced Deep Convolutional Neural Networks

## TL;DR

This paper presents an enhanced deep convolutional neural network that combines handcrafted features and data augmentation to accurately diagnose eight cardiac abnormalities from 12-lead ECGs, outperforming standard methods.

## Contribution

It introduces a novel approach integrating handcrafted features with deep learning and emphasizes the importance of data preprocessing for ECG classification.

## Key findings

- Achieved promising generalization performance in ECG abnormality classification.
- Demonstrated the effectiveness of combining handcrafted features with deep CNNs.
- Validated approach through empirical evaluation in a competitive setting.

## Abstract

We train an enhanced deep convolutional neural network in order to identify eight cardiac abnormalities from the standard 12-lead electrocardiograms (ECGs) using the dataset of 14000 ECGs. Instead of straightforwardly applying an end-to-end deep learning approach, we find that deep convolutional neural networks enhanced with sophisticated hand crafted features show advantages in reducing generalization errors. Additionally, data preprocessing and augmentation are essential since the distribution of eight cardiac abnormalities are highly biased in the given dataset. Our approach achieves promising generalization performance in the First China ECG Intelligent Competition; an empirical evaluation is also provided to validate the efficacy of our design on the competition ECG dataset.