## 4 Discussion  
In this research, a method was designed to analyze airflow signals for scoring apnea and hypopnea. According to the results, if only ACP (Fc = 1) is applied, the system provides considerably high sensitivity and low specificity, especially in patients with hypopnea. If SDA (Fc = 0) is applied, the system is highly influenced by noise such as body movement or sensor drops. As Figure 5 indicates, the dynamic threshold moves up when noise occurs, and this causes false alarms. Hence, using pure ACP (Fc = 1) or SDA (1-Fc = 0) may not be the correct decision, even with similar AHI numbers; 50% ACP and 50% SDA can provide the most reliable performances for scoring apnea and hypopnea when considering sensitivity, specificity, and accuracy. By contrast, pure SDA (Fc = 1) may be influenced by noise caused by leads being dropped or body movements, suddenly changing the dynamic threshold. Moreover, this method can be used with an embedded real-time system because it effectively reduces computational power requirements by focusing on time-domain features within 130 s window frames, requiring less signal processing in feature extraction, applying a dynamic threshold to adapt to real-time signal changes, and, most importantly, shortening the time required to build a fuzzy logic model without a training process. Reports have indicated that portable monitors measuring three or more physiological parameters (level 3 and level 2 monitoring) offer accurate results in comparison with laboratory results [35; 36; 37; 38; 39]. Due to the fact that discomfort and inconvenience are barriers to the prevalence of PSG among the public, a single-channel airflow signal is one of the most applicable solutions for real-time monitoring [32; 37; 38].  
OSAHS has serious and life-shortening consequences including cardiovascular disease, diabetes, poor quality of life, depression, and automobile accidents caused by falling asleep [39]. However, waiting times for PSG diagnosis in the United States and United Kingdom were estimated to be 2-10 and 7-60 months, respectively [40]. A study reported a median waiting time of 152 days in 2009 to 92 days in 2012 (\(p<0.0001\)) by home based PM [41]. A reliable PM may be used for general population screening as a result. Our study provides an airflow sensing algorithm with overall sensitivity of 74%, specificity of 100%, and accuracy of 80.0% for mild to moderate OSAHS. If the airflow in the PM screening of a patient represents an AHI of \(>\)15, the physician could preferentially arrange a PSG follow-up. Moreover, when applied to the airflow sensed by an oronasal thermal sensor under the mask, our algorithm may provide an accuracy of 85% and therefore assist with determining patients  
Figure 5: Dynamic threshold of SDA increases because of movement noise, which causes false alarms.  
with an AHI \(>\)5 and conducting PAP titration. However, the main limitation of the proposed system is that when fc = 0.5, it has low sensitivities of 54% and 38% at the thresholds of AHI \(>\)15 and AHI \(\geq\)30 among hypopnea-dominant individuals, respectively. We suggest if patients are tested as normal by this method, but combined with snoring and daytime letharge, physicians should further confirm with their PSG results. The characteristics of the proposed detection method are summarized in Table 6.