ReviewSensors (Basel, Switzerland)2020
A Survey of Heart Anomaly Detection Using Ambulatory Electrocardiogram (ECG).
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 20 papers, 1 of them a synthesis that pooled it.
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
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 110 citations in OpenAlex.
- A Systematic Review of Design of Electrodes and Interfaces for Non-Contact and Capacitive Biomedical Measurements: Terminology, Electrical Model, and System Analysis.Sensors (Basel, Switzerland) · 2026Pooled it
- Automated ECG Arrhythmia Classification Using Feature Images with Common Matrix Approach-Based Classifier.Sensors (Basel, Switzerland) · 2025Article
- Article
- Hybrid machine learning models for enhanced arrhythmia detection from ECG signals using autoencoder and convolution features.PloS one · 2025Article
- Development and validation of an ECG-based nomogram for early diagnosis of dilated cardiomyopathy.American journal of translational research · 2025Article
- Application of Online Anomaly Detection Using One-Class Classification to the Z24 Bridge.Sensors (Basel, Switzerland) · 2024Article
- Enhancing heart disease diagnosis through ECG image vectorization-based classification.Heliyon · 2024Article
- Review
- Article
- Temporal Analysis and Classification of Sensor Signals.Sensors (Basel, Switzerland) · 2023Article
- From Data to Diagnosis: How Machine Learning Is Changing Heart Health Monitoring.International journal of environmental research and public health · 2023Review
- On the Efficient Delivery and Storage of IoT Data in Edge-Fog-Cloud Environments.Sensors (Basel, Switzerland) · 2022Article
- Validation of Continuous Monitoring System for Epileptic Users in Outpatient Settings.Sensors (Basel, Switzerland) · 2022Article
- Structural Anomalies Detection from Electrocardiogram (ECG) with Spectrogram and Handcrafted Features.Sensors (Basel, Switzerland) · 2022Article
- Special Issue "Advanced Signal Processing in Wearable Sensors for Health Monitoring".Sensors (Basel, Switzerland) · 2022Article
- Computational Fluid Dynamics (CFD) For Predicting Pathological Changes In The Aorta: Is It Ready For Clinical Use?Arquivos brasileiros de cardiologia · 2022Article
- Utility of a novel wearable electrode embedded in an undershirt for electrocardiogram monitoring and detection of arrhythmias.PloS one · 2022Article
- A survey on outlier explanations.The VLDB journal : very large data bases : a publication of the VLDB Endowment · 2022Article
- An Automatic Method to Reduce Baseline Wander and Motion Artifacts on Ambulatory Electrocardiogram Signals.Sensors (Basel, Switzerland) · 2021Article
- Dynamic and conventional electrocardiograms for diagnosing arrhythmic coronary atherosclerotic heart disease: a comparative analysis.American journal of translational research · 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
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiovascular diseases (CVDs) are the number one cause of death globally. An estimated 17.9 million people die from CVDs each year, representing 31% of all global deaths. Most cardiac patients require early detection and treatment. Therefore, many products to monitor patient's heart conditions have been introduced on the market. Most of these devices can record a patient's bio-metric signals both in resting and in exercising situations. However, reading the massive amount of raw electrocardiogram (ECG) signals from the sensors is very time-consuming. Automatic anomaly detection for the ECG signals could act as an assistant for doctors to diagnose a cardiac condition. This paper reviews the current state-of-the-art of this technology discusses the pros and cons of the devices and algorithms found in the literature and the possible research directions to develop the next generation of ambulatory monitoring systems.
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.