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Arrhythmia is a condition characterized by an irregular heart rhythm, with ECG changes that differ based on its origin and nature. The types of arrhythmias discussed below include atrial, junctional, and ventricular arrhythmias.Atrial ArrhythmiasPremature Atrial Complexes (PACs): PACs are early atrial beats caused by stress, caffeine, alcohol, electrolyte imbalances, hypoxia, hyperthyroidism, or certain medications (e.g., bronchodilators and decongestants). The ECG shows early P waves with an...
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Introduction
An electrocardiogram (ECG) is a diagnostic tool for identifying cardiac conditions such as arrhythmias, conduction abnormalities, and myocardial ischemia.
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An electrocardiogram (ECG) visualizes the heart's electrical activity by tracing the electrical movement associated with each heartbeat on a graph or monitor. As the heart beats, an electrical wave passes through it, correlating with the cardiac cycle events.
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An electrocardiogram (ECG or EKG) is a critical diagnostic tool that records the electrical signals produced by the heart during each heartbeat. This recording is achieved through electrodes placed strategically on the arms, legs, and chest. The electrocardiograph amplifies these signals and produces 12 distinct tracings, offering a comprehensive understanding of the heart's electrical activity.
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Arrhythmias are irregular heart rhythms occurring when the heart's electrical impulses become abnormal. These disturbances can lead to various symptoms, depending on their severity and the underlying cause. Some common factors contributing to arrhythmias include hypoxia, ischemia, electrolyte imbalances, excessive catecholamine exposure, drug toxicity, and muscle overstretching. Arrhythmias can be classified into two main types based on the rate and site of origin of abnormal heart rhythms.
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Tachyarrhythmias are a type of dysrhythmia where the heart rate exceeds 100 beats per minute. Here are some common types of tachyarrhythmias:Sinus TachycardiaSinus tachycardia originates from increased impulses from the sinus node, leading to an elevated heart rate. It is often triggered by stress, fever, or exercise.Patients may experience palpitations, a sensation of a racing heart, dizziness, and chest discomfort.Causes and Risk Factors: Common causes include physical exertion, emotional...
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Dysrhythmias V: Evaluating Dysrhythmias01:30

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Dysrhythmias, also known as arrhythmias, are disturbances in the heart's rhythm that range from benign to life-threatening. A thorough evaluation is crucial for appropriate management and involves a comprehensive medical history, physical examination, and various diagnostic tests.Medical HistorySymptoms: Collect detailed information on palpitations, dizziness, syncope, chest pain, and fatigue. Note their onset, frequency, and triggers.Previous Cardiac Issues: Document any history of heart...
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ECG-Mamba: Cardiac Abnormality Classification With Non-Uniform-Mix Augmentation on 12-Lead ECGs.

Huawei Jiang1, Husna Mutahira2, Shibo Wei3

  • 1Department of Computer Science and EngineeringSungkyunkwan University Suwon 16419 South Korea.

IEEE Journal of Translational Engineering in Health and Medicine
|November 12, 2025
PubMed
Summary
This summary is machine-generated.

ECG-Mamba, a novel Mamba-based model, shows promise for detecting heart abnormalities from ECG signals. Its performance is enhanced by a new data augmentation technique, Non-Uniform-Mix, improving accuracy in cardiac diagnostics.

Keywords:
ECG-MambaHeart abnormality detectionnon-uniform operationsnon-uniform-mix

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Area of Science:

  • Artificial Intelligence
  • Biomedical Engineering
  • Cardiology

Background:

  • Electrocardiogram (ECG) analysis is crucial for diagnosing heart abnormalities.
  • ResNet and Transformer models are commonly used for ECG-based heart disease detection.
  • Selective State Space Models (SSMs) like Mamba offer efficient processing of long sequences, presenting an alternative to Transformers.

Purpose of the Study:

  • To propose ECG-Mamba, a Mamba-based model for detecting heart abnormalities from ECG signals.
  • To introduce a novel data augmentation technique, Non-Uniform-Mix, to improve Mamba's performance on ECG data.
  • To address the limitations of existing data augmentation methods with Mamba on ECG data.

Main Methods:

  • ECG-Mamba is developed based on Vision Mamba (Vim), utilizing a bidirectional SSM.
  • A conservative data augmentation strategy, Non-Uniform-Mix, is proposed to mitigate Mamba's sensitivity to noise.
  • MixUp is applied non-uniformly across epochs to a portion of the dataset.

Main Results:

  • ECG-Mamba outperforms leading algorithms from PhysioNet/CinC Challenges 2020 and 2021 in AUPRC and AUROC.
  • ECG-Mamba achieved a 16.6% higher AUPRC than the best 2021 algorithm on 12-lead ECGs.
  • The Non-Uniform-Mix augmentation further improved ECG-Mamba's AUPRC to 0.6271, a 2.8% increase.

Conclusions:

  • The Mamba-based ECG-Mamba model demonstrates significant potential for detecting cardiac abnormalities.
  • The proposed Non-Uniform-Mix augmentation effectively enhances model performance and addresses noise sensitivity.
  • ECG-Mamba offers a promising direction for advanced ECG analysis, with potential clinical impact in early diagnosis and telemedicine.