Efficient Deep Learning Based Hybrid Model to Detect Obstructive Sleep Apnea Article Swipe
Prashant Hemrajani
,
Vijaypal Singh Dhaka
,
Geeta Rani
,
Praveen Kumar Shukla
,
Durga Prasad Bavirisetti
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23104692
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23104692
An increasing number of patients and a lack of awareness about obstructive sleep apnea is a point of concern for the healthcare industry. Polysomnography is recommended by health experts to detect obstructive sleep apnea. The patient is paired up with devices that track patterns and activities during their sleep. Polysomnography, being a complex and expensive process, cannot be adopted by the majority of patients. Therefore, an alternative is required. The researchers devised various machine learning algorithms using single lead signals such as electrocardiogram, oxygen saturation, etc., for the detection of obstructive sleep apnea. These methods have low accuracy, less reliability, and high computation time. Thus, the authors introduced two different paradigms for the detection of obstructive sleep apnea. The first is MobileNet V1, and the other is the convergence of MobileNet V1 with two separate recurrent neural networks, Long-Short Term Memory and Gated Recurrent Unit. They evaluate the efficacy of their proposed method using authentic medical cases from the PhysioNet Apnea-Electrocardiogram database. The model MobileNet V1 achieves an accuracy of 89.5%, a convergence of MobileNet V1 with LSTM achieves an accuracy of 90%, and a convergence of MobileNet V1 with GRU achieves an accuracy of 90.29%. The obtained results prove the supremacy of the proposed approach in comparison to the state-of-the-art methods. To showcase the implementation of devised methods in a real-life scenario, the authors design a wearable device that monitors ECG signals and classifies them into apnea and normal. The device employs a security mechanism to transmit the ECG signals securely over the cloud with the consent of patients.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s23104692
- https://www.mdpi.com/1424-8220/23/10/4692/pdf?version=1683882700
- OA Status
- gold
- Cited By
- 27
- References
- 45
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4376275175
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4376275175Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s23104692Digital Object Identifier
- Title
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Efficient Deep Learning Based Hybrid Model to Detect Obstructive Sleep ApneaWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-05-12Full publication date if available
- Authors
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Prashant Hemrajani, Vijaypal Singh Dhaka, Geeta Rani, Praveen Kumar Shukla, Durga Prasad BavirisettiList of authors in order
- Landing page
-
https://doi.org/10.3390/s23104692Publisher landing page
- PDF URL
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https://www.mdpi.com/1424-8220/23/10/4692/pdf?version=1683882700Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.mdpi.com/1424-8220/23/10/4692/pdf?version=1683882700Direct OA link when available
- Concepts
-
Polysomnography, Obstructive sleep apnea, Computer science, Deep learning, Convergence (economics), Wearable computer, Artificial intelligence, Apnea, Sleep apnea, Machine learning, Sleep (system call), Medicine, Embedded system, Cardiology, Internal medicine, Economics, Operating system, Economic growthTop concepts (fields/topics) attached by OpenAlex
- Cited by
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27Total citation count in OpenAlex
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2025: 15, 2024: 10, 2023: 2Per-year citation counts (last 5 years)
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45Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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