Multimodality Multi-Lead ECG Arrhythmia Classification using Self-Supervised Learning

• URL: http://arxiv.org/abs/2210.06297v1
• Date: Fri, 30 Sep 2022 18:45:34 GMT
• Title: Multimodality Multi-Lead ECG Arrhythmia Classification using Self-Supervised Learning
• Authors: Thinh Phan, Duc Le, Patel Brijesh, Donald Adjeroh, Jingxian Wu, Morten Olgaard Jensen, Ngan Le
• Abstract summary: We propose SSL-based multimodality ECG classification. Our proposed network follows SSL learning paradigm and consists of two modules corresponding to pre-stream task, and down-stream task. To evaluate the effectiveness of our approach, ten-fold cross validation on the 12-lead PhysioNet 2020 dataset has been conducted.
• Score: 5.675787521359948
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Electrocardiogram (ECG) signal is one of the most effective sources of information mainly employed for the diagnosis and prediction of cardiovascular diseases (CVDs) connected with the abnormalities in heart rhythm. Clearly, single modality ECG (i.e. time series) cannot convey its complete characteristics, thus, exploiting both time and time-frequency modalities in the form of time-series data and spectrogram is needed. Leveraging the cutting-edge self-supervised learning (SSL) technique on unlabeled data, we propose SSL-based multimodality ECG classification. Our proposed network follows SSL learning paradigm and consists of two modules corresponding to pre-stream task, and down-stream task, respectively. In the SSL-pre-stream task, we utilize self-knowledge distillation (KD) techniques with no labeled data, on various transformations and in both time and frequency domains. In the down-stream task, which is trained on labeled data, we propose a gate fusion mechanism to fuse information from multimodality.To evaluate the effectiveness of our approach, ten-fold cross validation on the 12-lead PhysioNet 2020 dataset has been conducted.

Related papers

• FedCVD: The First Real-World Federated Learning Benchmark on Cardiovascular Disease Data [52.55123685248105]
  Cardiovascular diseases (CVDs) are currently the leading cause of death worldwide, highlighting the critical need for early diagnosis and treatment. Machine learning (ML) methods can help diagnose CVDs early, but their performance relies on access to substantial data with high quality. This paper presents the first real-world FL benchmark for cardiovascular disease detection, named FedCVD.
  arXiv  Detail & Related papers  (2024-10-28T02:24:01Z)
• Zero-Shot ECG Classification with Multimodal Learning and Test-time Clinical Knowledge Enhancement [10.611952462532908]
  Multimodal ECG Representation Learning (MERL) is capable of performing zero-shot ECG classification with text prompts. We propose the Clinical Knowledge Enhanced Prompt Engineering (CKEPE) approach to exploit external expert-verified clinical knowledge databases. MERL achieves an average AUC score of 75.2% in zero-shot classification (without training data), 3.2% higher than linear probed eSSL methods with 10% annotated training data, averaged across all six datasets.
  arXiv  Detail & Related papers  (2024-03-11T12:28:55Z)
• Guiding Masked Representation Learning to Capture Spatio-Temporal Relationship of Electrocardiogram [2.2842904085777045]
  We introduce ST-MEM (S-Temporal Masked Electrocardiogram Modeling), designed to learntemporal features by reconstructing 12-lead ECG data. ST-MEM outperforms other SSL baseline methods in various experimental settings for arrhythmia.
  arXiv  Detail & Related papers  (2024-02-02T10:04:13Z)
• Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training [75.40980802817349]
  Deep learning models have shown promise for automatically segmenting MS lesions, but the scarcity of accurately annotated data hinders progress in this area. We introduce a Decoupled Hard Label Correction (DHLC) strategy that considers the imbalanced distribution and fuzzy boundaries of MS lesions. We also introduce a Centrally Enhanced Label Correction (CELC) strategy, which leverages the aggregated central model as a correction teacher for all sites.
  arXiv  Detail & Related papers  (2023-08-31T00:36:10Z)
• ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning [20.465733855762835]
  Electrocardiogram (ECG) monitoring is one of the most powerful technique of cardiovascular disease (CVD) early identification. Classic rule-based algorithms are now completely outperformed by deep learning based methods. We propose a multi-resolution model that can sustain high-resolution low-level semantic information throughout.
  arXiv  Detail & Related papers  (2023-04-10T15:19:00Z)
• Frozen Language Model Helps ECG Zero-Shot Learning [12.974685769614062]
  We propose Multimodal ECG-Text Self-supervised pre-training (METS) We use a trainable ECG encoder and a frozen language model to embed paired ECG and automatically machine-generated clinical reports separately. In downstream classification tasks, METS achieves around 10% improvement in performance without using any annotated data.
  arXiv  Detail & Related papers  (2023-03-22T05:01:14Z)
• Hierarchical Deep Learning with Generative Adversarial Network for Automatic Cardiac Diagnosis from ECG Signals [2.5008947886814186]
  We propose a two-level hierarchical deep learning framework with Generative Adversarial Network (GAN) for automatic diagnosis of ECG signals. The first-level model is composed of a Memory-Augmented Deep auto-Encoder with GAN, which aims to differentiate abnormal signals from normal ECGs for anomaly detection. The second-level learning aims at robust multi-class classification for different arrhythmias identification.
  arXiv  Detail & Related papers  (2022-10-19T12:29:05Z)
• Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases [57.90226879210227]
  FedCy is a semi-supervised learning (FSSL) method that combines FL and self-supervised learning to exploit a decentralized dataset of both labeled and unlabeled videos. We demonstrate significant performance gains over state-of-the-art FSSL methods on the task of automatic recognition of surgical phases.
  arXiv  Detail & Related papers  (2022-03-14T17:44:53Z)
• 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)
• Uncovering the structure of clinical EEG signals with self-supervised learning [64.4754948595556]
  Supervised learning paradigms are often limited by the amount of labeled data that is available. This phenomenon is particularly problematic in clinically-relevant data, such as electroencephalography (EEG) By extracting information from unlabeled data, it might be possible to reach competitive performance with deep neural networks.
  arXiv  Detail & Related papers  (2020-07-31T14:34:47Z)
• 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.