Analysis of an adaptive lead weighted ResNet for multiclass classification of 12-lead ECGs

by   Zhibin Zhao, et al.

Background: Twelve lead ECGs are a core diagnostic tool for cardiovascular diseases. Here, we describe and analyse an ensemble deep neural network architecture to classify 24 cardiac abnormalities from 12-lead ECGs. Method: We proposed a squeeze and excite ResNet to automatically learn deep features from 12-lead ECGs, in order to identify 24 cardiac conditions. The deep features were augmented with age and gender features in the final fully connected layers. Output thresholds for each class were set using a constrained grid search. To determine why the model made incorrect predictions, two expert clinicians independently interpreted a random set of 100 misclassified ECGs concerning Left Axis Deviation. Results: Using the bespoke weighted accuracy metric, we achieved a 5-fold cross validation score of 0.684, and sensitivity and specificity of 0.758 and 0.969, respectively. We scored 0.520 on the full test data, and ranked 2nd out of 41 in the official challenge rankings. On a random set of misclassified ECGs, agreement between two clinicians and training labels was poor (clinician 1: kappa = -0.057, clinician 2: kappa = -0.159). In contrast, agreement between the clinicians was very high (kappa = 0.92). Discussion: The proposed prediction model performed well on the validation and hidden test data in comparison to models trained on the same data. We also discovered considerable inconsistency in training labels, which is likely to hinder development of more accurate models.


page 1

page 2

page 3

page 4


Reduced-Lead ECG Classifier Model Trained with DivideMix and Model Ensemble

Automatic diagnosis of multiple cardiac abnormalities from reduced-lead ...

A Mixed-Domain Self-Attention Network for Multilabel Cardiac Irregularity Classification Using Reduced-Lead Electrocardiogram

Electrocardiogram(ECG) is commonly used to detect cardiac irregularities...

Neural network-based coronary dominance classification of RCA angiograms

Background. Cardiac dominance classification is essential for SYNTAX sco...

Cardiac Arrhythmia Detection from ECG Combining Convolutional and Long Short-Term Memory Networks

Objectives: Atrial fibrillation (AF) is a common heart rhythm disorder a...

Predicting intelligence based on cortical WM/GM contrast, cortical thickness and volumetry

We propose a four-layer fully-connected neural network (FNN) for predict...

COVCOR20 at WNUT-2020 Task 2: An Attempt to Combine Deep Learning and Expert rules

In the scope of WNUT-2020 Task 2, we developed various text classificati...

Searching for Effective Neural Network Architectures for Heart Murmur Detection from Phonocardiogram

Aim: The George B. Moody PhysioNet Challenge 2022 raised problems of hea...

Please sign up or login with your details

Forgot password? Click here to reset