ArticleSensors (Basel, Switzerland)2024
Wearable ECG Device and Machine Learning for Heart Monitoring.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Identification of atrial fibrillation using heart rate variability: a meta-analysis.Frontiers in cardiovascular medicine · 2025Pooled it
- Development and Clinical Evaluation of a Wearable 12-Lead Electrocardiographic Platform with Automated ECG Analysis for Telemedicine Applications.Sensors (Basel, Switzerland) · 2026Article
- Advances in Remote Monitoring Technology Applications in Anesthesia: A Narrative Review.Medical science monitor : international medical journal of experimental and clinical research · 2026Review
- Development and Pilot Evaluation of a Wearable 12-Lead ECG System for Multilead Feature Analysis in Individuals with Different Glycemic Status.Sensors (Basel, Switzerland) · 2026Article
- The Evolving Role of Artificial Intelligence and Machine Learning in the Wearable Electrocardiogram: A Primer on Wearable-Enabled Prediction of Cardiac Dysfunction.Bioengineering (Basel, Switzerland) · 2026Review
- Mixture of checkpoint experts for explainable seizure detection using wearable devices.Scientific reports · 2025Article
- Systematic Review of Artificial Intelligence and Electrocardiography for Cardiovascular Disease Diagnosis.Bioengineering (Basel, Switzerland) · 2025Review
- Machine-Learning-Driven Phenotyping in Heart Failure with Preserved Ejection Fraction: Current Approaches and Future Directions.Medicina (Kaunas, Lithuania) · 2025Review
- Panic Attack Prediction for Patients With Panic Disorder via Machine Learning and Wearable Electrocardiography Monitoring: Model Development and Validation Study.Journal of medical Internet research · 2025Article
- Are Machine Learning methods effective in detecting undiagnosed atrial fibrillation in primary care settings using electronic health records? A systematic review.PLOS digital health · 2025Article
- Role of Mobile Health and Wearable Devices in Cardiovascular Disease Prevention: A Comprehensive Review.Cureus · 2025Review
- Advancements in Wearable and Implantable BioMEMS Devices: Transforming Healthcare Through Technology.Micromachines · 2025Review
- Reliable ECG Anomaly Detection on Edge Devices for Internet of Medical Things Applications.Sensors (Basel, Switzerland) · 2025Article
- Enhancing Connected Health Ecosystems Through IoT-Enabled Monitoring Technologies: A Case Study of the Monit4Healthy System.Sensors (Basel, Switzerland) · 2025Article
- Article
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.
Funding
Abstract
With cardiovascular diseases (CVD) remaining a leading cause of mortality, wearable devices for monitoring cardiac activity have gained significant, renewed interest among the medical community. This paper introduces an innovative ECG monitoring system based on a single-lead ECG machine, enhanced using machine learning methods. The system only processes and analyzes ECG data, but it can also be used to predict potential heart disease at an early stage. The wearable device was built on the ADS1298 and a microcontroller STM32L151xD. A server module based on the architecture style of the REST API was designed to facilitate interaction with the web-based segment of the system. The module is responsible for receiving data in real time from the microcontroller and delivering this data to the web-based segment of the module. Algorithms for analyzing ECG signals have been developed, including band filter artifact removal, K-means clustering for signal segmentation, and PQRST analysis. Machine learning methods, such as isolation forests, have been employed for ECG anomaly detection. Moreover, a comparative analysis with various machine learning methods, including logistic regression, random forest, SVM, XGBoost, decision forest, and CNNs, was conducted to predict the incidence of cardiovascular diseases. Convoluted neural networks (CNN) showed an accuracy of 0.926, proving their high effectiveness for ECG data processing.
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.