ArticleBiomedical signal processing and control2021
Detecting positional vertigo using an ensemble of 2D convolutional neural networks.
Article in Biomedical signal processing and control, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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
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Who cites it
3 citing papers in PubMed, 9 citations in OpenAlex.
- Diagnosis of Benign Paroxysmal Positional Vertigo: Review of Different Diagnostic Methods.Audiology research · 2026Review
- Diagnosing the benign paroxysmal positional vertigo via 1D and deep-learning composite model.Journal of neurology · 2023Article
- aEYE: A deep learning system for video nystagmus detection.Frontiers in neurology · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
The aim of the work presented here was to develop a system that can automatically identify attacks of dizziness occurring in patients suffering from positional vertigo, which occurs when sufferers move their head into certain positions. We used our novel medical device, CAVA, to record eye- and head-movement data continually for up to 30 days in patients diagnosed with a disorder called Benign Paroxysmal Positional Vertigo. Building upon our previous work, we describe a novel ensemble of five 2D Convolutional Neural Networks, using composite recognition features, including eye-movement data and three-channel accelerometer data. We achieve an
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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.