STUDY OBJECTIVE To develop an algorithm for improving apnea hypopnea index (AHI) estimation which includes event by event validation and event duration estimation. The algorithm uses breathing sounds, respiratory related movements and blood oxygen saturation (SaO2). METHODS Adults with suspected sleep apnea underwent overnight polysomnography (PSG) at Toronto Rehabilitations Institute. Simultaneously with PSG, breathing sounds and respiratory related movements were recorded over the suprasternal notch using the Patch. The Patch had a microphone and an accelerometer to record respiratory sounds and movement, respectively. First, we calculated the amount of drops in SaO2 from pulse oximeter. Subsequently, energy of breaths and accelerometer were extracted. Features were normalized, weighted, summed and passed through a threshold to estimate PatchAHI. PatchAHI was compared to the AHI obtained from PSG (PSGAHI). Furthermore, performance of event detection was evaluated using F1-score. Moreover, event duration difference between estimated and PSG-based events was compared. RESULTS Data from 69 subjects were investigated. PatchAHI had high correlation with PSGAHI (r2 = 0.88). Considering a diagnostic AHI cut-off of ≥15, sensitivity and specificity were 91.42 ± 11.92% and 89.29 ± 7.62%, respectively. F1-score for individual event detection increased from 0.22 ± 0.10 for AHI≤5 to 0.72 ± 0.09 for AHI >30. Moreover, event duration difference between estimated events and PSG-based events was 5.33 ± 8.17 sec. CONCLUSION Our proposed algorithm had high accuracy in estimating individual respiratory events during sleep. The algorithm can increase reliability of acoustic methods for diagnosis of sleep apnea at home.

中文翻译：

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通过对单个事件检测的验证，便携式诊断睡眠呼吸暂停。

目的研究开发一种改善呼吸暂停低通气指数（AHI）估计的算法，该算法包括逐事件验证和事件持续时间估计。该算法使用呼吸音，与呼吸有关的运动和血氧饱和度（SaO2）。方法疑似睡眠呼吸暂停的成年人在多伦多康复研究所接受了一夜多导睡眠图检查（PSG）。与PSG同时，使用Patch在胸骨上切口记录呼吸声音和与呼吸有关的运动。该贴片有一个麦克风和一个加速度计，分别记录呼吸声和运动。首先，我们从脉搏血氧仪计算出SaO2的滴落量。随后，提取呼吸能量和加速度计。功能已归一化，加权，求和并通过阈值以估计PatchAHI。将PatchAHI与从PSG（PSGAHI）获得的AHI进行了比较。此外，使用F1评分评估事件检测的性能。此外，比较了估计事件与基于PSG的事件之间的事件持续时间差异。结果调查了69位受试者的数据。PatchAHI与PSGAHI高度相关（r2 = 0.88）。考虑到诊断AHI截止值≥15，敏感性和特异性分别为91.42±11.92％和89.29±7.62％。单个事件检测的F1分数从AHI≤5的0.22±0.10增加到AHI> 30的0.72±0.09。此外，估计事件与基于PSG的事件之间的事件持续时间差异为5.33±8.17秒。结论我们提出的算法在估计睡眠期间的个体呼吸事件方面具有很高的准确性。