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Efficient Deep Learning Based Hybrid Model to Detect Obstructive Sleep Apnea

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 durin...

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Autores principales: Hemrajani, Prashant, Dhaka, Vijaypal Singh, Rani, Geeta, Shukla, Praveen, Bavirisetti, Durga Prasad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10224467/
https://www.ncbi.nlm.nih.gov/pubmed/37430605
http://dx.doi.org/10.3390/s23104692
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author Hemrajani, Prashant
Dhaka, Vijaypal Singh
Rani, Geeta
Shukla, Praveen
Bavirisetti, Durga Prasad
author_facet Hemrajani, Prashant
Dhaka, Vijaypal Singh
Rani, Geeta
Shukla, Praveen
Bavirisetti, Durga Prasad
author_sort Hemrajani, Prashant
collection PubMed
description 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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spelling pubmed-102244672023-05-28 Efficient Deep Learning Based Hybrid Model to Detect Obstructive Sleep Apnea Hemrajani, Prashant Dhaka, Vijaypal Singh Rani, Geeta Shukla, Praveen Bavirisetti, Durga Prasad Sensors (Basel) Article 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. MDPI 2023-05-12 /pmc/articles/PMC10224467/ /pubmed/37430605 http://dx.doi.org/10.3390/s23104692 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hemrajani, Prashant
Dhaka, Vijaypal Singh
Rani, Geeta
Shukla, Praveen
Bavirisetti, Durga Prasad
Efficient Deep Learning Based Hybrid Model to Detect Obstructive Sleep Apnea
title Efficient Deep Learning Based Hybrid Model to Detect Obstructive Sleep Apnea
title_full Efficient Deep Learning Based Hybrid Model to Detect Obstructive Sleep Apnea
title_fullStr Efficient Deep Learning Based Hybrid Model to Detect Obstructive Sleep Apnea
title_full_unstemmed Efficient Deep Learning Based Hybrid Model to Detect Obstructive Sleep Apnea
title_short Efficient Deep Learning Based Hybrid Model to Detect Obstructive Sleep Apnea
title_sort efficient deep learning based hybrid model to detect obstructive sleep apnea
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10224467/
https://www.ncbi.nlm.nih.gov/pubmed/37430605
http://dx.doi.org/10.3390/s23104692
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