Internet of Things Based Tired Detection using Deep Learning Techniques

Nijaguna G S, Sharanya S. Kumar, Devika Sv, Bechoo Lal, P. S
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Abstract

Sleep is essential for human survival since it helps to restore and maintain our bodies' immune systems and other essential processes. One-third of a person's life is devoted to sleeping, although few are aware of the many positive aspects of this activity. Two distinct types of sleep, REM and NREM, have been identified. A good night's rest is achieved when REM and NREM sleep alternate in a regular pattern. Disruptions to this cycle, whether they originate physiologically or psychologically, have been linked to a variety of health problems. Polysomnography (PSG) equipment is often used in sleep labs inside hospitals to perform sleep studies. A polysomnogram is an in-depth medical technique that records a patient's vital signs while they sleep and necessitates a hospital stay. Clinically, sleep apnea is defined as a breathing disease in which there are periodic pauses in breathing lasting 10 seconds or more that occur more than five times during the night. Sleep apnea may be classified as either Obstructive, Central, or Mixed. The prevalent sleep problem known as obstructive sleep apnea (OSA) is caused by the relaxation of muscles in the upper airway during sleep. The purpose of this study is to provide a technique for screening for Obstructive Sleep Apnea by analysing Heart Rate Variability of Electrocardiogram (ECG) data while the subject is asleep. The goals of this study are to create computational approaches for identifying OSA based on characteristics extracted from Heart Rate Variability (HRV) signals derived from sleep electrocardiograms (ECGs). Physio Net's Apnea-ECG recordings serve as the source for the ECG data.
基于深度学习技术的物联网疲劳检测
睡眠对人类的生存至关重要,因为它有助于恢复和维持我们身体的免疫系统和其他基本过程。人的一生有三分之一是用来睡觉的,尽管很少有人意识到这项活动的许多积极方面。人们已经确定了两种不同的睡眠类型:快速眼动睡眠和非快速眼动睡眠。当快速眼动睡眠和非快速眼动睡眠有规律地交替时,才能获得良好的夜间休息。这种循环的破坏,无论是生理上的还是心理上的,都与各种健康问题有关。多导睡眠描记仪(PSG)设备常用于医院的睡眠实验室进行睡眠研究。多导睡眠图是一项深入的医疗技术,可以在病人睡觉时记录他们的生命体征,这需要住院治疗。临床上,睡眠呼吸暂停被定义为一种呼吸系统疾病,在夜间发生5次以上的周期性呼吸暂停,持续10秒或更长时间。睡眠呼吸暂停可分为阻塞性、中枢性和混合性。普遍存在的睡眠问题是阻塞性睡眠呼吸暂停(OSA),它是由睡眠时上呼吸道肌肉松弛引起的。本研究的目的是通过分析受试者睡眠时的心电图(ECG)数据的心率变异性,提供一种筛查阻塞性睡眠呼吸暂停的技术。本研究的目的是创建基于从睡眠心电图(ECGs)中提取的心率变异性(HRV)信号的特征来识别OSA的计算方法。Physio Net的呼吸暂停-心电图记录作为心电图数据的来源。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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