Electrocardiogram (ECG) Based Cardiac Arrhythmia Detection and Classification using Machine Learning Algorithms

• URL: http://arxiv.org/abs/2412.05583v2
• Date: Tue, 10 Dec 2024 15:35:33 GMT
• Title: Electrocardiogram (ECG) Based Cardiac Arrhythmia Detection and Classification using Machine Learning Algorithms
• Authors: Atit Pokharel, Shashank Dahal, Pratik Sapkota, Bhupendra Bimal Chhetri,
• Abstract summary: Machine Learning (ML) and Deep Learning (DL) have opened new prospects in medical sciences for improved diagnosis, prognosis, and treatment of severe health conditions. This paper focuses on the development of an ML model with high predictive accuracy to classify arrhythmic electrocardiogram (ECG) signals.
• Score: 0.0
• License:
• Abstract: The rapid advancements in Artificial Intelligence, specifically Machine Learning (ML) and Deep Learning (DL), have opened new prospects in medical sciences for improved diagnosis, prognosis, and treatment of severe health conditions. This paper focuses on the development of an ML model with high predictive accuracy to classify arrhythmic electrocardiogram (ECG) signals. The ECG signals datasets utilized in this study were sourced from the PhysioNet and MIT-BIH databases. The research commenced with binary classification, where an optimized Bidirectional Long Short-Term Memory (Bi-LSTM) model yielded excellent results in differentiating normal and atrial fibrillation signals. A pivotal aspect of this research was a survey among medical professionals, which not only validated the practicality of AI-based ECG classifiers but also identified areas for improvement, including accuracy and the inclusion of more arrhythmia types. These insights drove the development of an advanced Convolutional Neural Network (CNN) system capable of classifying five different types of ECG signals with better accuracy and precision. The CNN model's robust performance was ensured through rigorous stratified 5-fold cross validation. A web portal was also developed to demonstrate real-world utility, offering access to the trained model for real-time classification. This study highlights the potential applications of such models in remote health monitoring, predictive healthcare, assistive diagnostic tools, and simulated environments for educational training and interdisciplinary collaboration between data scientists and medical personnel.

Related papers

• Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study [43.28613210217385]
  We employ and compare three state-of-the-art generative models to generate PCG data. Our results demonstrate that the generated PCG data closely resembles the original datasets. In our future work, we plan to incorporate this method into a data augmentation pipeline to synthesize abnormal PCG signals with heart murmurs.
  arXiv  Detail & Related papers  (2024-12-17T18:07:40Z)
• ECG Arrhythmia Detection Using Disease-specific Attention-based Deep Learning Model [0.0]
  We propose a disease-specific attention-based deep learning model (DANet) for arrhythmia detection from short ECG recordings. The novel idea is to introduce a soft-coding or hard-coding waveform enhanced module into existing deep neural networks. For the soft-coding DANet, we also develop a learning framework combining self-supervised pre-training with two-stage supervised training.
  arXiv  Detail & Related papers  (2024-07-25T13:27:10Z)
• MEIT: Multi-Modal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation [41.324530807795256]
  Electrocardiogram (ECG) is the primary non-invasive diagnostic tool for monitoring cardiac conditions. Recent studies have concentrated on classifying cardiac conditions using ECG data but have overlooked ECG report generation. We propose the Multimodal ECG Instruction Tuning (MEIT) framework, the first attempt to tackle ECG report generation with LLMs and multimodal instructions.
  arXiv  Detail & Related papers  (2024-03-07T23:20:56Z)
• 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)
• 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)
• GeoECG: Data Augmentation via Wasserstein Geodesic Perturbation for Robust Electrocardiogram Prediction [20.8603653664403]
  We propose a physiologically-inspired data augmentation method to improve performance and increase the robustness of heart disease detection based on ECG signals. We obtain augmented samples by perturbing the data distribution towards other classes along the geodesic in Wasserstein space. Learning from 12-lead ECG signals, our model is able to distinguish five categories of cardiac conditions.
  arXiv  Detail & Related papers  (2022-08-02T03:14:13Z)
• 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)
• DENS-ECG: A Deep Learning Approach for ECG Signal Delineation [15.648061765081264]
  This paper proposes a deep learning model for real-time segmentation of heartbeats. The proposed algorithm, named as the DENS-ECG algorithm, combines convolutional neural network (CNN) and long short-term memory (LSTM) model.
  arXiv  Detail & Related papers  (2020-05-18T13:13:41Z)
• 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)
• Multi-Lead ECG Classification via an Information-Based Attention Convolutional Neural Network [1.1720399305661802]
  One-dimensional convolutional neural networks (CNN) have proven to be effective in pervasive classification tasks. We implement the Residual connection and design a structure which can learn the weights from the information contained in different channels in the input feature map. An indicator named mean square deviation is introduced to monitor the performance of a particular model segment in the classification task.
  arXiv  Detail & Related papers  (2020-03-25T02:28:04Z)
• Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review [62.490310870300746]
  The electrocardiogram (ECG) is one of the most commonly used diagnostic tools in medicine and healthcare. Deep learning methods have achieved promising results on predictive healthcare tasks using ECG signals. This paper presents a systematic review of deep learning methods for ECG data from both modeling and application perspectives.
  arXiv  Detail & Related papers  (2019-12-28T02:44:29Z)

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.