基于规则的睡眠呼吸暂停检测算法

Luigi Pugliese, Michele Guagnano, Sara Groppo, Massimo Violante, Riccardo Groppo
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引用次数: 0

摘要

阻塞性睡眠呼吸暂停综合征(OSAS)是一种常见的睡眠障碍,其特征是睡眠中反复发作的呼吸停止。它会影响生活质量,并可能导致严重的健康并发症。持续监测心率变异性(HRV)和氧饱和度(SpO2)可以为睡眠呼吸暂停的存在和严重程度提供有价值的见解。本文提出的算法旨在识别OSAS的存在,然后高度准确地区分其严重程度(严重,中等或低)。在一个在线数据集上对该算法进行了评估;在算法评估结束时,相关系数达到98.65%。
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Rule-based Sleep-Apnea detection algorithm
Obstructive Sleep Apnea Syndrome (OSAS) is a common sleep disorder characterized by repeated episodes of breathing cessation during sleep. It affects the quality of life and can lead to severe health complications. Continuous monitoring of Heart Rate Variability (HRV) and oxygen saturation (SpO2) can provide valuable insights into the presence and severity of sleep apnea. The algorithm herein proposed aims to identify the presence of OSAS and then to highly accurately differentiate (Severe, Moderate or Low) its severity level. The algorithm was evaluated on an online dataset; at the end of the algorithm assessment, a correlation coefficient of 98.65% was reached.
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