ArticleJournal of healthcare engineering2022
ResNet-BiLSTM: A Multiscale Deep Learning Model for Heartbeat Detection Using Ballistocardiogram 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 It has been retracted, and should not be counted.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 3 papers.
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
3 citing papers in PubMed, 10 citations in OpenAlex.
- Heart rate detection method based on Ballistocardiogram signal of wearable device：Algorithm development and validation.Heliyon · 2024Article
- Surrogate modelling of heartbeat events for improved J-peak detection in BCG using deep learning.Frontiers in network physiology · 2024Article
- Retracted: ResNet-BiLSTM: A Multiscale Deep Learning Model for Heartbeat Detection Using Ballistocardiogram Signals.Journal of healthcare engineering · 2023Article
Corrections and comments
- Retraction · 2023-11-29Compromised Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As the heartbeat detection from ballistocardiogram (BCG) signals using force sensors is interfered by respiratory effort and artifact motion, advanced signal processing algorithms are required to detect the J-peak of each BCG signal so that beat-to-beat interval can be identified. However, existing methods generally rely on rule-based detection of a fixed size, without considering the rhythm features in a large time scale covering multiple BCG signals.
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.