One Dimensional CNN ECG Mamba for Multilabel Abnormality Classification in 12 Lead ECG

• URL: http://arxiv.org/abs/2510.13046v1
• Date: Tue, 14 Oct 2025 23:51:35 GMT
• Title: One Dimensional CNN ECG Mamba for Multilabel Abnormality Classification in 12 Lead ECG
• Authors: Huawei Jiang, Husna Mutahira, Gan Huang, Mannan Saeed Muhammad,
• Abstract summary: Comprehensive experiments on the PhysioNet Computing in Cardiology Challenges of 2020 and 2021 were conducted.<n>The proposed model achieved substantially higher AUPRC and AUROC scores than those reported by the best previously published algorithms.
• Score: 2.44657606551177
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate detection of cardiac abnormalities from electrocardiogram recordings is regarded as essential for clinical diagnostics and decision support. Traditional deep learning models such as residual networks and transformer architectures have been applied successfully to this task, but their performance has been limited when long sequential signals are processed. Recently, state space models have been introduced as an efficient alternative. In this study, a hybrid framework named One Dimensional Convolutional Neural Network Electrocardiogram Mamba is introduced, in which convolutional feature extraction is combined with Mamba, a selective state space model designed for effective sequence modeling. The model is built upon Vision Mamba, a bidirectional variant through which the representation of temporal dependencies in electrocardiogram data is enhanced. Comprehensive experiments on the PhysioNet Computing in Cardiology Challenges of 2020 and 2021 were conducted, and superior performance compared with existing methods was achieved. Specifically, the proposed model achieved substantially higher AUPRC and AUROC scores than those reported by the best previously published algorithms on twelve lead electrocardiograms. These results demonstrate the potential of Mamba-based architectures to advance reliable ECG classification. This capability supports early diagnosis and personalized treatment, while enhancing accessibility in telemedicine and resource-constrained healthcare systems.

Related papers

• ECG-IMN: Interpretable Mesomorphic Neural Networks for 12-Lead Electrocardiogram Interpretation [1.7152890789230053]
  Deep learning has achieved expert-level performance in automated electrocardiogram (ECG) diagnosis, yet the "black-box" nature of these models hinders their clinical deployment.<n>We propose the ECG-IMN, an Interpretable Mesomorphic Neural Network tailored for high-resolution 12-lead ECG classification.
  arXiv  Detail & Related papers  (2026-02-10T09:17:29Z)
• Dual-Model Weight Selection and Self-Knowledge Distillation for Medical Image Classification [47.17249726328169]
  We propose a novel medical image classification method that integrates dual-model weight selection with self-knowledge distillation (SKD)<n>Our method overcomes the limitations of conventional approaches, which often fail to retain critical information in compact models.
  arXiv  Detail & Related papers  (2025-08-28T06:15:06Z)
• Electrocardiogram (ECG) Based Cardiac Arrhythmia Detection and Classification using Machine Learning Algorithms [0.0]
  Machine Learning (ML) and Deep Learning (DL) have opened new prospects in medical sciences for improved diagnosis, prognosis, and treatment of severe health conditions.<n>This paper focuses on the development of an ML model with high predictive accuracy to classify arrhythmic electrocardiogram (ECG) signals.
  arXiv  Detail & Related papers  (2024-12-07T08:29:44Z)
• MambaCapsule: Towards Transparent Cardiac Disease Diagnosis with Electrocardiography Using Mamba Capsule Network [16.562266471455672]
  This paper introduces MambaCapsule, a deep neural networks for ECG arrhythmias classification. MambaCapsule has achieved a total accuracy of 99.54% and 99.59% on the test sets respectively.
  arXiv  Detail & Related papers  (2024-07-30T15:12:29Z)
• ECGMamba: Towards Efficient ECG Classification with BiSSM [3.0120310355085467]
  We propose a novel model, ECGMamba, which employs a bidirectional state-space model (BiSSM) to enhance classification efficiency. The experimental results on two publicly available ECG datasets demonstrate that ECGMamba effectively balances the effectiveness and efficiency of classification.
  arXiv  Detail & Related papers  (2024-06-14T14:55:53Z)
• EKGNet: A 10.96{\mu}W Fully Analog Neural Network for Intra-Patient Arrhythmia Classification [79.7946379395238]
  We present an integrated approach by combining analog computing and deep learning for electrocardiogram (ECG) arrhythmia classification. We propose EKGNet, a hardware-efficient and fully analog arrhythmia classification architecture that archives high accuracy with low power consumption.
  arXiv  Detail & Related papers  (2023-10-24T02:37:49Z)
• 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)
• A multi-stage machine learning model on diagnosis of esophageal manometry [50.591267188664666]
  The framework includes deep-learning models at the swallow-level stage and feature-based machine learning models at the study-level stage. This is the first artificial-intelligence-style model to automatically predict CC diagnosis of HRM study from raw multi-swallow data.
  arXiv  Detail & Related papers  (2021-06-25T20:09:23Z)
• Heart-Darts: Classification of Heartbeats Using Differentiable Architecture Search [22.225051965963114]
  Arrhythmia is a cardiovascular disease that manifests irregular heartbeats. In arrhythmia detection, the electrocardiogram (ECG) signal is an important diagnostic technique. With the application of convolutional neural networks (CNNs), the evaluation process has been accelerated and the performance is improved.
  arXiv  Detail & Related papers  (2021-05-03T08:57:48Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• 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.