ReviewSensors (Basel, Switzerland)2020
Computational Diagnostic Techniques for Electrocardiogram Signal Analysis.
Review in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
What it found
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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
35 citing papers in PubMed, 1 synthesis or guideline pooled it, 133 citations in OpenAlex.
- Accuracy of Machine Learning in Detecting Pediatric Epileptic Seizures: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- A multifaceted approach for obstructive sleep apnea classification from ECG signal using deep learning.Npj biological timing and sleep · 2026Article
- Integrating Artificial Intelligence with Wearable Sensors for Advanced Health Monitoring and Diagnosis.Biosensors · 2026Review
- Diagnostic Approaches to Cardiac Arrhythmias: Integrating Electrocardiography and Advanced Monitoring.Cureus · 2026Review
- Early prediction of intraoperative hypotension: development and validation of the HypoBridCast hybrid deep learning model.BMC anesthesiology · 2026Article
- xGNN4MI: explainability of graph neural networks in 12-lead electrocardiography for cardiovascular disease classification.NPJ digital medicine · 2026Article
- Diabetes-Related Alterations in Vectorcardiographic Repolarization Geometry: Insights from Physiological and Machine-Learning Analysis.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Article
- Comparative evaluation of filtration techniques for ECG signal denoising with emphasis on stationary wavelet transform.Scientific reports · 2025Article
- Technological Advances and Medical Applications of Implantable Electronic Devices: From the Heart, Brain, and Skin to Gastrointestinal Organs.Biosensors · 2025Review
- Efficient Keyset Design for Neural Networks Using Homomorphic Encryption.Sensors (Basel, Switzerland) · 2025Article
- Emerging rapid detection methods for the monitoring of cardiovascular diseases: Current trends and future perspectives.Materials today. Bio · 2025Review
- Comparison of diagnostic accuracy of electrocardiogram-based versus photoplethysmography-based smartwatches for atrial fibrillation detection: A Systematic Review and Meta-Analysis.Annals of medicine and surgery (2012) · 2025Article
- Automated ECG Arrhythmia Classification Using Feature Images with Common Matrix Approach-Based Classifier.Sensors (Basel, Switzerland) · 2025Article
- A knowledge embedded multimodal pseudo-siamese model for atrial fibrillation detection.Scientific reports · 2025Article
- Clinically interpretable multiclass neural network for discriminating cardiac diseases.Heliyon · 2025Article
- A coordinated adaptive multiscale enhanced spatio-temporal fusion network for multi-lead electrocardiogram arrhythmia detection.Scientific reports · 2024Article
- Development and Validation of a Real-Time Service Model for Noise Removal and Arrhythmia Classification Using Electrocardiogram Signals.Sensors (Basel, Switzerland) · 2024Article
- Super-resolution techniques for biomedical applications and challenges.Biomedical engineering letters · 2024Review
- Arrhythmia detection by the graph convolution network and a proposed structure for communication between cardiac leads.BMC medical research methodology · 2024Article
- A Low-Noise Low-Power 0.001Hz-1kHz Neural Recording System-on-Chip With Sample-Level Duty-Cycling.IEEE transactions on biomedical circuits and systems · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Cardiovascular diseases (CVDs), including asymptomatic myocardial ischemia, angina, myocardial infarction, and ischemic heart failure, are the leading cause of death globally. Early detection and treatment of CVDs significantly contribute to the prevention or delay of cardiovascular death. Electrocardiogram (ECG) records the electrical impulses generated by heart muscles, which reflect regular or irregular beating activity. Computer-aided techniques provide fast and accurate tools to identify CVDs using a patient's ECG signal, which have achieved great success in recent years. Latest computational diagnostic techniques based on ECG signals for estimating CVDs conditions are summarized here. The procedure of ECG signals analysis is discussed in several subsections, including data preprocessing, feature engineering, classification, and application. In particular, the End-to-End models integrate feature extraction and classification into learning algorithms, which not only greatly simplifies the process of data analysis, but also shows excellent accuracy and robustness. Portable devices enable users to monitor their cardiovascular status at any time, bringing new scenarios as well as challenges to the application of ECG algorithms. Computational diagnostic techniques for ECG signal analysis show great potential for helping health care professionals, and their application in daily life benefits both patients and sub-healthy people.
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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.