ArticleComputational and mathematical methods in medicine2018
Mobile Personal Health Monitoring for Automated Classification of Electrocardiogram Signals in Elderly.
Article in Computational and mathematical methods in medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of mHealth Devices for Atrial Fibrillation Screening: Systematic Review.JMIR mHealth and uHealth · 2019Pooled it
- Predictive Accuracy of a Clinical and Genetic Risk Model for Atrial Fibrillation.Circulation. Genomic and precision medicine · 2021Trial
- IoT-based emergency cardiac death risk rescue alert system.MethodsX · 2024Article
- Computer Simulation of Catheter Cryoablation for Pulmonary Vein Isolation.Healthcare (Basel, Switzerland) · 2024Article
- Wearable ECG Device and Machine Learning for Heart Monitoring.Sensors (Basel, Switzerland) · 2024Article
- Article
- Trends in Passive IoT Biomarker Monitoring and Machine Learning for Cardiovascular Disease Management in the U.S. Elderly Population.Advances in geriatric medicine and research · 2023Article
- Cost-effectiveness of Screening for Atrial Fibrillation Using Wearable Devices.JAMA health forum · 2022Article
- Machine Learning for Cardiovascular Outcomes From Wearable Data: Systematic Review From a Technology Readiness Level Point of View.JMIR medical informatics · 2022Review
- Advances in telemedicine for the management of the elderly cardiac patient.Journal of geriatric cardiology : JGC · 2021Article
- Comparative Clinical Effectiveness of Population-Based Atrial Fibrillation Screening Using Contemporary Modalities: A Decision-Analytic Model.Journal of the American Heart Association · 2021Article
- Population-Based Screening for Atrial Fibrillation.Circulation research · 2020Review
- ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges.Sensors (Basel, Switzerland) · 2020Review
- Segmentation of the ECG Signal by Means of a Linear Regression Algorithm.Sensors (Basel, Switzerland) · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Mobile electrocardiogram (ECG) monitoring is an emerging area that has received increasing attention in recent years, but still real-life validation for elderly residing in low and middle-income countries is scarce. We developed a wearable ECG monitor that is integrated with a self-designed wireless sensor for ECG signal acquisition. It is used with a native purposely designed smartphone application, based on machine learning techniques, for automated classification of captured ECG beats from aged people. When tested on 100 older adults, the monitoring system discriminated normal and abnormal ECG signals with a high degree of accuracy (97%), sensitivity (100%), and specificity (96.6%). With further verification, the system could be useful for detecting cardiac abnormalities in the home environment and contribute to prevention, early diagnosis, and effective treatment of cardiovascular diseases, while keeping costs down and increasing access to healthcare services for older persons.
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.