## 3 Results  
A total of 60 participants, most of whom were male (N = 57, 95%), were enrolled. The age of the participants was \(45.2\pm 12.2\) years, and the AHI score was \(40.8\pm 31.8\) (Table 1). The body mass index, neck circumference, and waist circumference were \(29.7\pm 6.3\) kg/m\({}^{2}\), \(40.0\pm 3.8\) cm, and \(100.1\pm 11.4\) cm, respectively. Apnea and hypopnea indexes were \(21.5\pm 26.3\) and \(19.3\pm 15.2\), respectively. There were a total of 12,391 episodes, with apnea events of 6445 and hypopnea events of 5946. Figure 3 offers a standard example to describe the warming and no-warming situations. The top plot depicts a single channel airflow waveform containing two apnea events; the middle plot depicts ACP index curves and two horizontal thresholds (dashed lines), which is updated every 5 s; and the bottom plot depicts SDA index curves and two horizontal thresholds (dashed lines), also updated every 5 s. In the AHI warning areas, the fuzzy combination risk of ACP and SDA was over 80% with weights Fc and 1-Fc (Equation (7)), respectively. However, in the center of the no warning area, the high ACP indexes were balanced out by SDA indexes in our algorithm, because SDA provides the variation of airflow signals and ACP. The ACP is sensitive to slight changes in the amplitude of peaks, which may be influenced by noise and unstable breath patterns. By contrast, the SDA with dynamic threshold better distinguishes quiet and active breathing patterns. Figure 3: Standard example of fusion results. The results revealed that a lower Fc value was related to a lower AHI value, whereas a higher Fc value coincided with a higher AHI value (Table 2). When Fc was at 40%, the predicted value was \(25.7\pm 20.7\), and the difference to AHI was \(-15.2\pm 17.7\); when Fc was at 100%, predicted and difference values were \(75.3\pm 19.1\) and \(34.4\pm 36.8\), respectively. Detection values presented two extremes with the Fc filter. Noticeably, when Fc was between 50% and 70%, the detection difference was relatively small. As applied for determining OSAHS severity, Sp could not classify normal breathing or OSAHS of any severity with any Fc (Table 3). However, Sn and Sp were 74% and 100%, respectively, at the AHI threshold of \(>\)15, and 71% and 97% for the detection of severe OSAHS (AHI \(\geq\) 30) with Fc at 50% (Table 3). Although the detection performances of Fc at 60% or 70% were almost the same as Fc at 50%, Sp was 0% for an AHI of \(>\)15 among apnea-dominant participants (Table 4). As a result, Fc at 50% offered optimal airflow detection for mild to severe OSAHS. However, Sn of 54% and Sp of 100% were observed for an AHI of \(>\)15 and Sn of 38% and Sp of 100% were detected for an AHI of \(\geq\)30 among hypopnea-dominant participants (Table 5). The results indicate that a higher Fc resulted in a higher Sn and lower Sp for OSAHS of any severity (Figure 4). \begin{table}
\begin{tabular}{c c c c} \hline
**Item** & **Mean \(\pm\) SD** & **Item** & **Mean \(\pm\) SD** \\ \hline AHI & \(40.8\pm 31.8\) & R (\%) & \(12.0\pm 7.7\) \\ Apnea-hypopnea event & \(206.5\pm 159.5\) & N1 (\%) & \(25.5\pm 19.6\) \\ Apnea index & \(21.5\pm 26.3\) & N2 (\%) & \(55.0\pm 16.3\) \\ Apnea event & \(107.4\pm 129.0\) & N3 (\%) & \(7.4\pm 7.1\) \\ Hypopnea index & \(19.3\pm 15.2\) & SpO\({}_{2}\)nidar (\%) & \(77.0\pm 11.7\) \\ Hypopnea event & \(99.1\pm 81.0\) & ESS & \(10.2\pm 4.4\) \\ TRT (min) & \(357.8\pm 11.3\) & Age (years old) & \(45.2\pm 12.2\) \\ TST (min) & \(307.4\pm 37.6\) & BMI (kg/m\({}^{2}\)) & \(29.7\pm 6.3\) \\ Sleep efficiency (\%) & \(85.9\pm 10.2\) & NC (cm) & \(40.0\pm 3.8\) \\ Arousal index & \(42.5\pm 31.3\) & WC (cm) & \(100.1\pm 11.4\) \\ \hline \end{tabular}
\end{table} 
Table 1: Basic characteristics of enrolled participants (N = 60; female/male = 3/57).