Sleep Breath (2015) 19:599–605 DOI 10.1007/s11325-014-1055-0
ORIGINAL ARTICLE
Nocturnal snoring sound analysis in the diagnosis of obstructive sleep apnea in the Chinese Han population Huajun Xu & Wei Song & Hongliang Yi & Limin Hou & Changheng Zhang & Bin Chen & Yuqin Chen & Shankai Yin
Received: 3 July 2014 / Revised: 7 August 2014 / Accepted: 26 August 2014 / Published online: 9 September 2014 # Springer-Verlag Berlin Heidelberg 2014
Abstract Purpose Loud snoring is one of the principle symptoms of obstructive sleep apnea (OSA). Snoring sound analysis is a potentially cost-effective, reliable alternative for the diagnosis of OSA. However, no investigation has determined the accuracy of snoring signal analysis for the diagnosis of OSA in the Chinese Han population. Therefore, we investigated whether whole-night snoring detection and analysis aids the diagnosis of OSA using a new snore analysis technique. Methods Snoring sounds were recorded using a non-contact microphone and polysomnography (PSG) was performed simultaneously throughout the night. We randomly selected 30 subjects each from four groups based on the severity of OSA. The rhythm and frequency domain of the snoring signal were analyzed based on frequency energy endpoint detection (FEP) and the Earth mover’s distance (EMD), for each subject to harvest the EMD-calculated Apnea-Hypopnea Index (AHIEMD). Finally, we compared the AHIEMD with the PSGmonitored AHI (AHIPSG). Results The accuracy of the AHIEMD compared with the AHIPSG was 96.7, 86.7, 86.7, and 96.7 % in non-, mild, moderate, and severe OSA patients, respectively. AHIEMD was correlated with AHIPSG (r2 =0.950, p10 s, this is recorded as apnea or hypopnea (this is called the first determination). Then, we determined the type of snoring after the STI (candidate snoring). We scored the apnea or hypopnea as false or true according to whether simple snoring or the snoring of OSA occurred, respectively (this is called the second determination). The second determination was based on the EMD analysis. The second determination of apnea/hypopnea events (EMD analysis) In order to quantify the difference in the distribution of the energy spectrum between simple snoring and the snoring of OSA, we split the simple snoring in each non-OSA patient into ten fragments (300 fragments in total) or five fragments for the snoring of OSA in each OSA patient (450 fragments in total). We then chose 350 OSA snoring fragments from 450 total fragments for further analysis.
ð6Þ
d ij f ij
i¼1 j¼1 4 X 8 X
ð7Þ f ij
i¼1 j¼1
If the nth EMD ≥ threshold of EMD, we defined the candidate snore as true (snoring of OSA); otherwise, it was false (simple snoring) (the second determination). Finally, with this snoring analysis, we can capture every apnea and hypopnea event correctly. Then, EMD-calculated AHI (AHIEMD) was calculated as the number of apnea/hypopnea events per hour of sleep. Statistical analysis Statistical analysis was performed using the SPSS (ver. 17.0) and MedCalc (ver. 12.7.3) statistical software packages. The correlation between AHI EMD and PSG-monitored AHI (AHIPSG) was evaluated using Pearson’s correlation coefficient. The diagnostic sensitivity and specificity were determined using receiver operating characteristic (ROC) curve analysis. The similarities between AHIEMD and AHIPSG were assessed using Bland–Altman analysis. A p value of
ORIGINAL ARTICLE
Nocturnal snoring sound analysis in the diagnosis of obstructive sleep apnea in the Chinese Han population Huajun Xu & Wei Song & Hongliang Yi & Limin Hou & Changheng Zhang & Bin Chen & Yuqin Chen & Shankai Yin
Received: 3 July 2014 / Revised: 7 August 2014 / Accepted: 26 August 2014 / Published online: 9 September 2014 # Springer-Verlag Berlin Heidelberg 2014
Abstract Purpose Loud snoring is one of the principle symptoms of obstructive sleep apnea (OSA). Snoring sound analysis is a potentially cost-effective, reliable alternative for the diagnosis of OSA. However, no investigation has determined the accuracy of snoring signal analysis for the diagnosis of OSA in the Chinese Han population. Therefore, we investigated whether whole-night snoring detection and analysis aids the diagnosis of OSA using a new snore analysis technique. Methods Snoring sounds were recorded using a non-contact microphone and polysomnography (PSG) was performed simultaneously throughout the night. We randomly selected 30 subjects each from four groups based on the severity of OSA. The rhythm and frequency domain of the snoring signal were analyzed based on frequency energy endpoint detection (FEP) and the Earth mover’s distance (EMD), for each subject to harvest the EMD-calculated Apnea-Hypopnea Index (AHIEMD). Finally, we compared the AHIEMD with the PSGmonitored AHI (AHIPSG). Results The accuracy of the AHIEMD compared with the AHIPSG was 96.7, 86.7, 86.7, and 96.7 % in non-, mild, moderate, and severe OSA patients, respectively. AHIEMD was correlated with AHIPSG (r2 =0.950, p10 s, this is recorded as apnea or hypopnea (this is called the first determination). Then, we determined the type of snoring after the STI (candidate snoring). We scored the apnea or hypopnea as false or true according to whether simple snoring or the snoring of OSA occurred, respectively (this is called the second determination). The second determination was based on the EMD analysis. The second determination of apnea/hypopnea events (EMD analysis) In order to quantify the difference in the distribution of the energy spectrum between simple snoring and the snoring of OSA, we split the simple snoring in each non-OSA patient into ten fragments (300 fragments in total) or five fragments for the snoring of OSA in each OSA patient (450 fragments in total). We then chose 350 OSA snoring fragments from 450 total fragments for further analysis.
ð6Þ
d ij f ij
i¼1 j¼1 4 X 8 X
ð7Þ f ij
i¼1 j¼1
If the nth EMD ≥ threshold of EMD, we defined the candidate snore as true (snoring of OSA); otherwise, it was false (simple snoring) (the second determination). Finally, with this snoring analysis, we can capture every apnea and hypopnea event correctly. Then, EMD-calculated AHI (AHIEMD) was calculated as the number of apnea/hypopnea events per hour of sleep. Statistical analysis Statistical analysis was performed using the SPSS (ver. 17.0) and MedCalc (ver. 12.7.3) statistical software packages. The correlation between AHI EMD and PSG-monitored AHI (AHIPSG) was evaluated using Pearson’s correlation coefficient. The diagnostic sensitivity and specificity were determined using receiver operating characteristic (ROC) curve analysis. The similarities between AHIEMD and AHIPSG were assessed using Bland–Altman analysis. A p value of
