ArticleFrontiers in computational neuroscience2020
A Study on Arrhythmia via ECG Signal Classification Using the Convolutional Neural Network.
Article in Frontiers in computational neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed.
- Deep Neural Networks for Automatic Atrial Fibrillation Detection Using Long-Term Ambulatory Electrocardiography: Retrospective Diagnostic Accuracy Study.JMIR cardio · 2026Article
- A hybrid model combining 1D-CNN and BERT for intelligent ECG arrhythmia classification.Scientific reports · 2025Article
- Arrhythmia classification based on multi-input convolutional neural network with attention mechanism.PloS one · 2025Article
- Leveraging electrocardiography signals for deep learning-driven cardiovascular disease classification model.Heliyon · 2024Article
- A graph-based cardiac arrhythmia classification methodology using one-lead ECG recordings.Intelligent systems with applications · 2024Article
- Improving Adversarial Robustness of ECG Classification Based on Lipschitz Constraints and Channel Activation Suppression.Sensors (Basel, Switzerland) · 2024Article
- Artificial intelligence applied in cardiovascular disease: a bibliometric and visual analysis.Frontiers in cardiovascular medicine · 2024Article
- Electrocardiogram-based deep learning algorithm for the screening of obstructive coronary artery disease.BMC cardiovascular disorders · 2023Article
- An ECG Stitching Scheme for Driver Arrhythmia Classification Based on Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- Evaluation of a decided sample size in machine learning applications.BMC bioinformatics · 2023Article
- ECG Classification Using an Optimal Temporal Convolutional Network for Remote Health Monitoring.Sensors (Basel, Switzerland) · 2023Article
- Method for Solving Difficulties in Rhythm Classification Caused by Few Samples and Similar Characteristics in Electrocardiograms.Bioengineering (Basel, Switzerland) · 2023Article
- Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review.Heliyon · 2023Review
- IoT Based System for Heart Monitoring and Arrhythmia Detection Using Machine Learning.Journal of healthcare engineering · 2023Article
- State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.JMIR medical informatics · 2022Review
- Heartbeat Classification and Arrhythmia Detection Using a Multi-Model Deep-Learning Technique.Sensors (Basel, Switzerland) · 2022Article
- Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs.Sensors (Basel, Switzerland) · 2022Article
- Convolutional Neural Networks for Mechanistic Driver Detection in Atrial Fibrillation.International journal of molecular sciences · 2022Article
- ECG Data Analysis with Denoising Approach and Customized CNNs.Sensors (Basel, Switzerland) · 2022Article
- FPGA-Based High-Performance Phonocardiography System for Extraction of Cardiac Sound Components Using Inverse Delayed Neuron Model.Frontiers in medical technology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiovascular diseases (CVDs) are the leading cause of death today. The current identification method of the diseases is analyzing the Electrocardiogram (ECG), which is a medical monitoring technology recording cardiac activity. Unfortunately, looking for experts to analyze a large amount of ECG data consumes too many medical resources. Therefore, the method of identifying ECG characteristics based on machine learning has gradually become prevalent. However, there are some drawbacks to these typical methods, requiring manual feature recognition, complex models, and long training time. This paper proposes a robust and efficient 12-layer deep one-dimensional convolutional neural network on classifying the five micro-classes of heartbeat types in the MIT- BIH Arrhythmia database. The five types of heartbeat features are classified, and wavelet self-adaptive threshold denoising method is used in the experiments. Compared with BP neural network, random forest, and other CNN networks, the results show that the model proposed in this paper has better performance in accuracy, sensitivity, robustness, and anti-noise capability. Its accurate classification effectively saves medical resources, which has a positive effect on clinical practice.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.