ArticleJournal of healthcare engineering2022
An Early Warning of Atrial Fibrillation Based on Short-Time ECG Signals.
Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. 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, 4 citations in OpenAlex.
- Retracted: An Early Warning of Atrial Fibrillation Based on Short-Time ECG Signals.Journal of healthcare engineering · 2023Article
Corrections and comments
- Retraction · 2023-01-19Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Objections by Author(s) · · See also: https://pubpeer.com/publications/FA08B172017F228DF219D9F455BB19
- Retracted
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study introduces a method to classify single-lead ECG signals by extracting features through traditional methods and deep neural network methods. At first step, the statistical type features of the ECG signals are exacted by traditional methods, including time domain features, frequency domain features, and medical domain features. And then, deep neural networks are used to extract the deeper features of the ECG signal. The database of ECG signals is from Cinc 17, which have 8528 samples of short-time ECG signal. The huge amount of data makes the classification and identification more accurate by atrial fibrillation, normal sinus rhythm, noise, and indiscernible. Compare the base model built by the classified data and the data collected by the ECG device of CareON to enable daily early screening and a remote alert function with WeChat app. This method can extend the prevention, detection, and diagnosis of heart disease to the family, company, and other out-of-hospital scenarios, thus enabling faster treatment of heart patients and saving medical resources.
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.