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

• URL: http://arxiv.org/abs/2112.01496v1
• Date: Wed, 1 Dec 2021 15:44:52 GMT
• Title: Analysis of an adaptive lead weighted ResNet for multiclass classification of 12-lead ECGs
• Authors: Zhibin Zhao, Darcy Murphy, Hugh Gifford, Stefan Williams, Annie Darlington, Samuel D. Relton, Hui Fang, David C. Wong
• Abstract summary: We describe and analyse an ensemble deep neural network architecture to classify 24 cardiac abnormalities from 12-lead ECGs. We achieved a 5-fold cross validation score of 0.684, and sensitivity and specificity of 0.758 and 0.969, respectively.
• Score: 1.155818089388109
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• ECG-SMART-NET: A Deep Learning Architecture for Precise ECG Diagnosis of Occlusion Myocardial Infarction [1.7894680263068135]
  We describe ECG--NET for identification of myocardial infarction (OMI) OMI is a severe form of heart attack characterized by complete blockage of one or more coronary arteries. Two thirds of OMI cases are difficult to visually identify from a 12-lead electrocardiogram.
  arXiv  Detail & Related papers  (2024-05-08T19:59:16Z)
• Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies [62.840338941861134]
  We propose to leverage transfer learning from large datasets annotated by radiologists, to predict the histological score available on a small annex dataset. We compare different pretraining methods, namely weakly-supervised and self-supervised ones, to improve the prediction of the cirrhosis. This method outperforms the baseline classification of the METAVIR score, reaching an AUC of 0.84 and a balanced accuracy of 0.75.
  arXiv  Detail & Related papers  (2023-02-16T17:06:23Z)
• A Mixed-Domain Self-Attention Network for Multilabel Cardiac Irregularity Classification Using Reduced-Lead Electrocardiogram [10.351641831500924]
  This study proposes Mixed-Domain Self-Attention Resnet (MDARsn) to identify cardiac abnormalities from reduced-lead ECG. Our classifiers received scores of 0.602, 0.593, 0.597, 0.591, and 0.589 for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead versions of the hidden validation set.
  arXiv  Detail & Related papers  (2022-04-29T07:35:08Z)
• Generalizing electrocardiogram delineation: training convolutional neural networks with synthetic data augmentation [63.51064808536065]
  Existing databases for ECG delineation are small, being insufficient in size and in the array of pathological conditions they represent. This article delves has two main contributions. First, a pseudo-synthetic data generation algorithm was developed, based in probabilistically composing ECG traces given "pools" of fundamental segments, as cropped from the original databases, and a set of rules for their arrangement into coherent synthetic traces. Second, two novel segmentation-based loss functions have been developed, which attempt at enforcing the prediction of an exact number of independent structures and at producing closer segmentation boundaries by focusing on a reduced number of samples.
  arXiv  Detail & Related papers  (2021-11-25T10:11:41Z)
• Reduced-Lead ECG Classifier Model Trained with DivideMix and Model Ensemble [2.6736079396231807]
  We propose deep neural network (DNN)-based ECG models that incorporate DivideMix and weight averaging (SWA) DivideMix was used to refine the noisy label by using two separate models. SWA was used to enhance the effect of the models generated by DivideMix.
  arXiv  Detail & Related papers  (2021-09-24T16:41:27Z)
• Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade oftwo U-nets: training and assessment on multipledatasets using different annotation criteria [83.83783947027392]
  This study aims at exploiting Artificial intelligence (AI) for the identification, segmentation and quantification of COVID-19 pulmonary lesions. We developed an automated analysis pipeline, the LungQuant system, based on a cascade of two U-nets. The accuracy in predicting the CT-Severity Score (CT-SS) of the LungQuant system has been also evaluated.
  arXiv  Detail & Related papers  (2021-05-06T10:21:28Z)
• Identification of Ischemic Heart Disease by using machine learning technique based on parameters measuring Heart Rate Variability [50.591267188664666]
  In this study, 18 non-invasive features (age, gender, left ventricular ejection fraction and 15 obtained from HRV) of 243 subjects were used to train and validate a series of several ANN. The best result was obtained using 7 input parameters and 7 hidden nodes with an accuracy of 98.9% and 82% for the training and validation dataset.
  arXiv  Detail & Related papers  (2020-10-29T19:14:41Z)
• Multilabel 12-Lead Electrocardiogram Classification Using Gradient Boosting Tree Ensemble [64.29529357862955]
  We build an algorithm using gradient boosted tree ensembles fitted on morphology and signal processing features to classify ECG diagnosis. For each lead, we derive features from heart rate variability, PQRST template shape, and the full signal waveform. We join the features of all 12 leads to fit an ensemble of gradient boosting decision trees to predict probabilities of ECG instances belonging to each class.
  arXiv  Detail & Related papers  (2020-10-21T18:11:36Z)
• Interpretable Deep Learning for Automatic Diagnosis of 12-lead Electrocardiogram [15.464768773761527]
  We developed a deep neural network for multi-label classification of cardiac arrhythmias in 12-lead ECG recordings. The proposed model achieved an average area under the receiver operating characteristic curve (AUC) of 0.970 and an average F1 score of 0.813. The best-performing leads are lead I, aVR, and V5 among 12 leads.
  arXiv  Detail & Related papers  (2020-10-20T14:51:00Z)
• ECG-DelNet: Delineation of Ambulatory Electrocardiograms with Mixed Quality Labeling Using Neural Networks [69.25956542388653]
  Deep learning (DL) algorithms are gaining weight in academic and industrial settings. We demonstrate DL can be successfully applied to low interpretative tasks by embedding ECG detection and delineation onto a segmentation framework. The model was trained using PhysioNet's QT database, comprised of 105 ambulatory ECG recordings.
  arXiv  Detail & Related papers  (2020-05-11T16:29:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.