Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings

Abstract

Objective: We aim to combine deep neural networks and engineered features (hand-crafted features based on medical domain knowledge) for cardiac arrhythmia detection from short single-lead ECG recordings. Approach: We propose a two-stage method named for cardiac arrhythmia detection. The first stage is feature extraction and the second stage is classifier building. In the feature extraction stage, we extract both deep features and engineered features. Deep features are obtained by modifying deep neural networks into a deep feature extractor. Engineered features are extracted by summarizing existing approaches into four feature groups. Then, we propose a feature aggregation approach to combine these features. In the classifier building stage, we build multiple gradient boosting decision trees and combine them to get the final detector. Main results: Experiments are performed on the PhysioNet/Computing in Cardiology Challenge 2017 dataset (Clifford et al 2017 Computing in Cardiology vol 44). Using F1 scores reported on the hidden test set as measurements, got 0.9117 on Normal (F1N), 0.8128 on Atrial Fibrillation (AF) (F1A), 0.7505 on Others (F1O), and 0.5671 on Noise (F1P). It placed 5th in the Challenge and 8th in the follow-up challenge (ranked by considering the average of Normal, AF, and Others (F1NAO = 0.825)). When rounding to two decimal places, we were part a three-way tie for 1st place and were part a seven-way tie for 2nd place in the follow-up challenge. Further experiments show that combined features perform better than individual features, and deep features show more importance scores than other features. Significance: can benefit from both feature engineering-based methods and recent deep neural networks. It is flexible and can easily assimilate the ability of new cardiac arrhythmia detection methods.

MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals

“…Many of them have demonstrated stateof-the-art performance due to their ability in extracting effective features [Rajpurkar et al, 2017;Hong et al, 2017]. Some of them build an end-to-end classifier [Kiranyaz et al, 2016;Rajpurkar et al, 2017;Zihlmann et al, 2017], others build a mixture model which combines traditional feature engineering methods and deep models [Hong et al, 2017;Schwab et al, 2017;Hong et al, 2019]. However, existing deep models are insufficient in three aspects.…”

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

“…It has a wide variety of applications such as biometrics authentication, object detection, classification, compression, image classification, and other computer vision related technology fields. Deep learning has great potential of applications in cardiology such as ECG arrhythmia detection with Deep-CNN [71], [72], [74], [76], [77], [79], [80], Robust Deep Dictionary Language (RDDL) [73], Deep Brief Network with Restricted Boltzmann Machine (DBN+RBM) [75] and Deep Neural Network (DNN) [78]. MI detection is performed with Deep-CNN [81] and Deep Neural Network (DNN) [82] while detecting heartbeats is performed by DNN in [83].…”

Prediction and prognosis of acute myocardial infarction in patients with previous coronary artery bypass grafting using neural network model

“…Neural networks are a form of artificial intelligence and they may obviate some of the problems associated with traditional statistical techniques, and they are representing a major advance in predictive modeling [10][11][12][13]. Neural networks can find hidden features in input patterns that are not visible by conventional statistical methods.…”