ArticleJournal of biomedical physics & engineering2025
Portable Holter with Cloud-Based Learning Analytics for Real-Time Health Monitoring.
Article in Journal of biomedical physics & engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
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
No grant is acknowledged in the PubMed record.
Abstract
The increasing prevalence of cardiovascular diseases underscores the need for efficient and user-friendly tools to monitor heart health. Traditional Holter monitors, while effective, are often bulky and inconvenient, limiting their use in real-world scenarios. This study introduces the Smart Portable Holter, a wireless device designed for real-time cardiac monitoring, enabling early detection of heart irregularities with enhanced accuracy and user convenience. The device captures continuous electrocardiogram signals and transmits them to a secure cloud platform for processing. Machine learning models, including Random Forest and Extreme Gradient Boosting (XGBoost), analyze the data to detect cardiac events. The system's performance was evaluated using real-world datasets, emphasizing accuracy and reliability in identifying cardiac arrhythmias. The Smart Portable Holter delivers an impressive 98% accuracy in detecting cardiac events. Its compact and wireless design enhances user comfort, allowing for seamless wear throughout the day. Coupled with advanced analytics, it offers detailed, time-stamped records that empower both users and healthcare professionals. These features facilitated early diagnosis and supported personalized treatment planning for patients with varying cardiac conditions. The Smart Portable Holter represents a significant advancement in cardiac care, combining portability, real-time analytics, and high diagnostic accuracy. By empowering patients and healthcare providers with actionable insights, it fosters proactive heart health management and contributes to improved clinical outcomes.
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.