ArticleSensors (Basel, Switzerland)2020
Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 42 citations in OpenAlex.
- Transformer-based deep learning approach for obstructive sleep apnea detection using single-lead ECG.Frontiers in artificial intelligence · 2026Article
- Advanced sleep disorder detection using multi-layered ensemble learning and advanced data balancing techniques.Frontiers in artificial intelligence · 2024Article
- Age-integrated artificial intelligence framework for sleep stage classification and obstructive sleep apnea screening.Frontiers in neuroscience · 2023Article
- A Novel Sparse Linear Mixed Model for Multi-Source Mixed-Frequency Data Fusion in Telemedicine.IISE transactions on healthcare systems engineering · 2023Article
- State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review.JMIR medical informatics · 2022Review
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) have been used for recognition of breathing and movement-related sleep disorders. Bio-signal processing has been performed by extracting EMG features exploiting entropy and statistical moments, in addition to developing an iterative pulse peak detection algorithm using synchrosqueezed wavelet transform (SSWT) for reliable extraction of heart rate and breathing-related features from ECG. A deep learning framework has been designed to incorporate EMG and ECG features. The framework has been used to classify four groups: healthy subjects, patients with obstructive sleep apnea (OSA), patients with restless leg syndrome (RLS) and patients with both OSA and RLS. The proposed deep learning framework produced a mean accuracy of 72% and weighted F1 score of 0.57 across subjects for our formulated four-class problem.
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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.