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首页> 外文会议>Annual International Conference of the IEEE Engineering in Medicine and Biology Society >Usefulness of Spectral Analysis of Respiratory Rate Variability to Help in Pediatric Sleep Apnea-Hypopnea Syndrome Diagnosis
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Usefulness of Spectral Analysis of Respiratory Rate Variability to Help in Pediatric Sleep Apnea-Hypopnea Syndrome Diagnosis

机译：呼吸速率变异性光谱分析的有用性，有助于儿科睡眠呼吸暂停症综合征诊断

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The sleep apnea-hypopnea syndrome (SAHS) is a chronic respiratory disorder of high prevalence among children (up to 4%). Nocturnal polysomnography (PSG) is the gold standard method to diagnose SAHS, which is a complex, expensive, and time-consuming test. Consequently, alternative simplified methods are demanded. We propose the analysis of the respiratory rate variability (RRV) signal, directly obtained from the airflow (AF) signals. The aim of our study is to evaluate the usefulness of the spectral information obtained from RRV in the diagnosis of pediatric SAHS. A database composed of 946 AF and blood oxygen saturation (SpO2) recordings from children between 0 and 13 years old was used. Our database was divided into four severity groups according to the apnea-hipopnea index (AHI): no-SAHS (AHI < 1 events/h), mild (1 events/h ≤ AHI < 5 events/h), moderate (5 events/h ≤ AHI < 10 events/h), and severe SAHS (AHI ≥ 10 events/h). RRV and 3% oxygen desaturation index (ODI3) were obtained from AF and SpO2 recordings, respectively. A spectral band of interest was determined (0.09–0.20 Hz.) and a total of 12 spectral features were extracted. Nine of these features showed statistically significant differences (p-value < 0.05) among the four severity groups. The spectral features from RRV along with ODI3 were used as inputs to binary logistic regression (LR) classifiers. The diagnostic performance of LR models were evaluated for the AHI cut-off points of 1, 5, and 10 e/h, achieving 66.5%, 84.0%, and 88.5% accuracy, respectively. These results outperformed those obtained by single ODI3. The joint use of the spectral information from RRV and ODI3 achieved a high diagnostic capability in the most severely-affected children, thus showing their complementarity. These results suggest that the information contained in RRV spectrum together with ODI3 is useful to help identify moderate-to-severe SAHS.
机译：睡眠呼吸暂停症综合征（SAHS）是儿童患有高患病率的慢性呼吸系统障碍（高达4％）。 Nocturnal PolySomNography（PSG）是诊断SAHS的黄金标准方法，这是一种复杂，昂贵和耗时的测试。因此，需要替代的简化方法。我们提出了从气流（AF）信号直接获得的呼吸速率变异性（RRV）信号的分析。我们的研究目的是评估从RRV获得的频谱信息在儿科SAHS的诊断中获得的有用性。使用由946 AF和血氧饱和度（SPO2）的数据库，从0到13岁之间的儿童录音。根据呼吸暂停症（AHI），我们的数据库分为四个严重性群体：No-Sahs（AHI <1 Events / h），轻度（1个事件/h≤ahi<5事件/ h），中等（5个事件/h≤ahi<10事件/ h）和严重的sahs（ahi≥10个事件/ h）。 RRV和3％氧去饱和指数（ODI3）分别从AF和SPO2录音中获得。测定兴趣的光谱带（0.09-0.20Hz。），提取了总共12种光谱特征。这些特征中的九种在四个严重性群体中显示出统计上显着的差异（p值<0.05）。 RRV以及ODI3的光谱特征用作二进制逻辑回归（LR）分类器的输入。评价LR模型的诊断性能为1,5和10 e / h的AHI截止点，分别实现66.5％，84.0％和88.5％的精度。这些结果优于单个ODI3获得的结果。来自RRV和ODI3的光谱信息的联合使用在受影响最严重的儿童中实现了高诊断能力，从而呈现它们的互补性。这些结果表明，RRV光谱中含有的信息与ODI3一起是有助于帮助鉴定中等至重度的SAH。

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