Development of a support vector machine learning and smart phone Internet of Things-based architecture for real-time sle
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RESEARCH
Development of a support vector machine learning and smart phone Internet of Things‑based architecture for real‑time sleep apnea diagnosis Bin Ma1, Zhaolong Wu1, Shengyu Li2, Ryan Benton2, Dongqi Li2, Yulong Huang3, Mohan Vamsi Kasukurthi2, Jingwei Lin4, Glen M. Borchert5, Shaobo Tan2, Gang Li1, Meihong Yang1* and Jingshan Huang2,5*
From 10th International Workshop on Biomedical and Health Informatics San Diego, CA, USA. 18-20 November 2019
Abstract Background: The breathing disorder obstructive sleep apnea syndrome (OSAS) only occurs while asleep. While polysomnography (PSG) represents the premiere standard for diagnosing OSAS, it is quite costly, complicated to use, and carries a significant delay between testing and diagnosis. Methods: This work describes a novel architecture and algorithm designed to efficiently diagnose OSAS via the use of smart phones. In our algorithm, features are extracted from the data, specifically blood oxygen saturation as represented by SpO2. These features are used by a support vector machine (SVM) based strategy to create a classification model. The resultant SVM classification model can then be employed to diagnose OSAS. To allow remote diagnosis, we have combined a simple monitoring system with our algorithm. The system allows physiological data to be obtained from a smart phone, the data to be uploaded to the cloud for processing, and finally population of a diagnostic report sent back to the smart phone in real-time. Results: Our initial evaluation of this algorithm utilizing actual patient data finds its sensitivity, accuracy, and specificity to be 87.6%, 90.2%, and 94.1%, respectively. Discussion: Our architecture can monitor human physiological readings in real time and give early warning of abnormal physiological parameters. Moreover, after our evaluation, we find 5G technology offers higher bandwidth with lower delays ensuring more effective monitoring. In addition, we evaluate our algorithm utilizing real-world data; the proposed approach has high accuracy, sensitivity, and specific, demonstrating that our approach is very promising.
*Correspondence: [email protected]; [email protected] 1 Shandong Provincial Key Laboratory of Computer Networks, Qilu University of Technology (Shandong Academy of Science), Jinan, China 5 College of Medicine, University of South Alabama, Mobile, AL 36688, USA Full list of author information is available at the end of the article © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Crea
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