1. Introduction
Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a major public health concern characterized by recurrence of airflow reduction (hypopnea) or cessation (apnea) due to upper airway collapse during sleep, resulting in oxygen desaturation and fragmented sleep [
1,
2,
3]. In clinical practice, the severity of OSAHS is categorized as mild, moderate, or severe, with apnea-hypopnea index (AHI) thresholds of 5, 15, or 30, respectively, following gold standard polysomnography (PSG) diagnosis [
4].
The prevalence of OSAHS for the general population ranges from 9% to 38% [
5]. Global estimates suggest that 936 million people worldwide are affected by mild to severe OSAHS, and 425 million people aged between 30 and 69 years worldwide have moderate to severe OSAHS [
6]. It is associated with systemic hypertension and chronic inflammation and leads to impaired quality of life [
7,
8,
9]. Studies have reported that OSAHS severity increases patient morbidity and mortality from cardiovascular diseases, end-stage renal disease (ESRD), and hypertension [
10,
11,
12]. Due to the fact that OSAHS causes excessive daytime sleepiness and decreased alertness, it increases the risk of automobile accidents [
13]. To treat moderate to severe OSAHS as early as possible, portable monitoring (PM) using level 3 or 4 PSG with fewer sensing channels is recommended to screen for OSAHS and shorten waiting lists for the gold standard PSG [
14].
Airflow is an essential indicator used for scoring apnea and hypopnea [
14,
15,
16]. It is measured using a thermistor/thermocouple (Th) or nasal pressure (Np) [
17]. Digital biomarkers provide a novel approach for risk management. Digital biomarkers in health and disease management are collected using digital health technologies to explain, influence, or predict health-related outcomes [
18]. Due to the fact that the dynamic response of a Th is slow and the relationship between electrical signal and airflow is nonlinear, a single channel airflow biomarker is used to detect apnea [
15,
17]. By contrast, Np exhibits an excellent dynamic response and is suitable for accurately quantifying the magnitude of flow, and thereby detecting hypopnea. Unattended PM with Np in conjunction with a Th is more sensitive than a Th or Np alone in detecting sleep-disordered breathing [
19,
20]. Moreover, studies have found that PM with Np alone significantly improves apnea and hypopnea detection and classification over PM with a Th [
21,
22]. However, one study concluded that if only an Np sensor was used to detect apnea and hypopnea, apnea index and AHI were overestimated. Hence, an algorithm with a Th is potential to produce results that conform precisely to guidelines [
19].
Positive airway pressure (PAP) is recommended for the treatment of moderate to severe OSAHS [
23]. To determine optimal pressure, PAP titration is manually performed with standard overnight PSG. According to the clinical signs of apnea or hypopnea, PAP may be increased to eliminate these obstructive respiratory events and to identify optimal pressure for an AHI of <5. However, when the titration reference is based on the airflow measured by the device or by the pressure difference between the mask and the outlet of the machine, as measured by a pressure transducer, the optimal titration point is overestimated [
24]. In this case, the use of a Th may affect titration reference choice, because its electrical characteristics depend on temperature change caused by breathing, and a small Th may be placed under the mask with less leakage when PAP is being conducted.
Due to the use of manual scoring, apnea and hypopnea duration time within a subject can vary markedly by from 10 to 120 s [
15]. Accordingly, time domain features for the detection of apnea and hypopnea are now fully accepted by physicians. The time domain algorithm with amplitude changes of peaks (ACP) of Np has been reported in [
25,
26]. The sensitivity (Sn) and specificity (Sp) were 78.47% and 79.86%, respectively, for detecting severe OSAHS [
25]; however, the algorithm’s accuracy for detecting mild to moderate OSAHS was not reported. In addition, the detection performance in a subject with more apnea or hypopnea episodes determined using the same filter remains unknown. Adjusting the filtering of the ACP of airflow may more reliably detect an apnea-hypopnea episode than the Th used in the study [
25]. This may improve the reference accuracy for titration and provide a more sensitive PM for OSAHS screening.
Though PM with an oronasal thermal sensor is necessary for OSAHS, the accuracy of detection remains low. We hypothesized that applying single-channel signals could have a good performance for simultaneous detection of apnea and hypopnea. Thus, the study aimed to develop a screening method by using single-channel airflow indexes derived from the Th for OSAHS detection.
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 ± 12.2 years, and the AHI score was 40.8 ± 31.8 (
Table 1). The body mass index, neck circumference, and waist circumference were 29.7 ± 6.3 kg/m
2, 40.0 ± 3.8 cm, and 100.1 ± 11.4 cm, respectively. Apnea and hypopnea indexes were 21.5 ± 26.3 and 19.3 ± 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.
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 ± 20.7, and the difference to AHI was −15.2 ± 17.7; when Fc was at 100%, predicted and difference values were 75.3 ± 19.1 and 34.4 ± 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 ≥ 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 ≥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).
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 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 ≥30 among hypopnea-dominant individuals, respectively. We suggest if patients are tested as normal by this method, but combined with snoring and daytime lethargy, physicians should further confirm with their PSG results. The characteristics of the proposed detection method are summarized in
Table 6.